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Agile, Waterfall, and Lean are just a few of the project-centric methodologies for software development that you'll find in this Zone. Whether your team is focused on goals like achieving greater speed, having well-defined project scopes, or using fewer resources, the approach you adopt will offer clear guidelines to help structure your team's work. In this Zone, you'll find resources on user stories, implementation examples, and more to help you decide which methodology is the best fit and apply it in your development practices.
Most Software Developers in Test are familiar with Test-Driven Development, or TDD, but Behavior-Driven Development, or BDD, is often misunderstood. The truth is that both of these approaches have advantages and disadvantages to consider. This blog deep dives into TDD vs. BDD comparison by looking at both these approaches individually. Later, we would compare them to functionalities, supported frameworks, and more.I could say BDD is merely the evolution of TDD. What Is Test-Driven Development (TDD)? Test-Driven Development is a programming practice implemented from a developer’s perspective. In this process, a QA engineer begins designing and writing test cases for every small functionality of an application. This technique tries to answer a simple question — Is the code valid? The primary purpose of this practice is to modify or write a new code only when the tests fail. Consequently, it results in lesser duplication of test scripts. This technique is mainly popular in agile development ecosystems. In a TDD approach, automated test scripts are written before functional pieces of code. How To Implement Test-Driven Development (TDD) Test-driven development gives preference to testing instead of the implementation phase. As all the tests pass, it signals the end of the first iteration. However, if more features have to be implemented, all the phases must be repeated with new feature tests. The figure below summarizes the flow of TDD: Pros and Cons of Test-Driven Development (TDD) TDD is a test-first approach, where automated test scripts are typically written before implementing the product’s actual features. However, TDD has its own share of advantages (or pros) and disadvantages(or cons) Advantages of Test-Driven Development (TDD) Reduced cost of development: The development process in TDD is divided into smaller chunks to simplify the detection of issues at the early stages of design and development. Focus on design and architecture: Writing tests before the implementation makes the development process more seamless and efficient. Improved code coverage: Through TDD, a well-designed system can achieve 100 percent code coverage. Code visibility: Tests are written to verify smaller functionalities, making it easy to refactor and maintain the code. Detailed documentation: Since tests are written for verifying micro-level functionalities, writing documentation becomes an easy task. Disadvantages of Test-Driven Development (TDD) Bugs leading to faulty code: Tests could contain bugs which in turn results in faulty implementation. This can be averted by using the right BDD framework, performing detailed code reviews, and more. Costly architectural mistakes: If the generated test code is not in line with the desired architecture, it could result in huge losses. Slowness in development: Creating test cases before code development slows product development. In addition, framing test cases may take a huge amount of time as the actual implementation is not available at that time. Requires prior experience: Prior experience with TDD is a must since many teams commit the mistake of not running tests at the Red Stage. In this section, we have covered what TDD is, including its advantages and disadvantages. In the next section, we will look into BDD. What Is Behavior-Driven Development (BDD)? Behavioral-Driven Development (BDD) is derived from the Test-Driven Development (TDD) methodology. In BDD, tests are based on systems behavior. The BDD approach describes different ways to develop a feature based on its behavior. In most cases, the Given-When-Then approach is used for writing test cases. You can learn more about Gherkin by reading the article on Behavior Driven Development By Selenium Testing With Gherkin. Let’s take an example for better understanding: Scenario: User Can Sign In Given a valid user with username “lambdatest1.” When I log in as “lambdatest1.” Then I should see the message “Welcome, lambdatest1” Here is the overall approach to BDD: Debugging the errors in the latter stages of the development life cycle often proves to be very expensive. In most cases, ambiguity in understanding the requirements is the root cause behind this. Therefore, one must ensure that all the development efforts remain aligned toward fulfilling pre-determined requirements. BDD allows developers to do the above by: Allowing the requirements to be defined in a standard approach using simple English. Providing several ways to illustrate real-world scenarios for understanding requirements. Providing a platform that enables the tech and non-tech teams to collaborate and understand the requirements How To Implement Behavior-Driven Development (BDD) As we know, BDD is an extension of TDD. BDD plays a crucial role in cutting back on the bugs and errors you would encounter at later stages of product development. Effective test automation strategy, including scenarios, can be developed by involving different teams (e.g., engineering, product management, marketing, etc.). The BDD approach accords technical and non-technical teams with the collaboration of knowledge and ideas. It’s time for some action. Cucumber is a tool that supports BDD; anyone can write specifications in plain English using Gherkin. It is as simple as adding a test from the business value point of view, as I like to call business-centric Test Automation. If you are new to Cucumber, you can also check out the step-by-step tutorial on Selenium Cucumber with examples. Let’s start by adding the Cucumber plugin using npm to our current Cypress Testing project. 1. Please install the plugin. JavaScript npm install --save-dev cypress-cucumber-preprocessor 2. The following dependency with the latest version will be added to your package.json of the project. At the time of writing this recipe, the version of cypress-cucumber-preprocessor is 4.1.4. JavaScript devDependencies": { "cypress-cucumber-preprocessor": "^4.1.4" } 3. To make it work, we would need to add it to Cypress plugins as part of Cypress Configuration under cypress/plugins/index.js JavaScript const cucumber = required('cypress-cucumber-preprocessor').default JavaScript module.exports = (on, config) => { on('file:preprocessor', cucumber()) } 4. Next, we need to add cosmiconfig configuration to the package.json. Cosmiconfig searches and loads the required configuration of the project. In this case, we are defining to locate the step definitions by setting up the below property. JavaScript "cypress-cucumber-preprocessor": { "nonGlobalStepDefinitions": true } 5. Let’s create a new folder under Cypress -> Integration directory as ‘cucumber-test’ and then create a new feature, “Home.feature.” JavaScript Feature: Home / Landing Page Scenario: Navigating to E-commerce Store Given I open home page Then I should see Homepage 6. For step definition location, let’s create a folder named “home.” Let’s create a step definition file ‘homeSteps.js.” JavaScript import { Given } from "cypress-cucumber-preprocessor/steps"; Given('I open home page', () => { cy.visit('https://ecommerce-playground.lambdatest.io/') }) Then('I should see Homepage', () => { cy.get('#search').should('be.visible') }) 1. The folder structure should be as the following: 7. Now, let’s run using the following command in the Terminal or command console to execute the test: JavaScript npx cypress open 8. On Cypress Test runner, select ‘home.feature.’ And you should see the results correctly: Note: You can download the code from the GitHub Repository. Gherkin Keywords Keywords are used to give structure & meaning to the executable specifications. Every specification or a feature file starts one of the keywords as: Feature Scenario Scenario Outline Background Feature: It is the primary key in the Gherkin; it is used to describe the specification name. Gherkin Feature: Hello In order to start a conversation with someone chat bot needs to start with greetings so the user gets the interactive feel. Scenario: It is the collection of actions or steps that need to be performed to fulfill a test objective. Gherkin Feature: Greetings In order to start the conversation with someone chat bot needs to start with greetings so the user gets the interactive feel. Scenario: example Given greeting has been set to "hello" When name is "Frank" Then greetings equals "Hello Frank" Scenario Outline: is used when the same scenario needs to be executed with multiple data sets. Scenario Outline is always defined with the Examples keyword. That’s where data sets can be defined to execute the same scenario multiple times. Scenario Outline is used for Data-driven tests. Gherkin Feature: Greetings In order to start the conversation with someone chat bot needs to start with greetings so the user gets the interactive feel. Scenario Outline: example Given greeting has been set to"" When name is "" Then greetings equals "" Examples: | greeting | name | conversation | | Hello | Frank | Hello Frank | | Hi | Maria | Hi Maria | | Hey | Johnny | Hey Johnny | Implementation example: Gherkin let myName, greeting, finalword Given("greeting has been set to {string}", setGreeting => { greeting = setGreeting + ' ' }) When("name is {string}", setName => { myName = setName finalString = greeting.concat(myName) }) Then("greetings equals {string}", expectedValue => { expect(finalword).to.equal(expectedValue) }) Background: It is used to define a step or series of steps common to all the tests included in the feature file. The steps defined as part of the background get executed before each scenario. Adding the Background section is helpful to avoid duplication of feature steps. Gherkin Feature: Background Example Background: Given greeting has been set Scenario: example1 When name is "Frank" Then greetings equals "Hello Frank" Scenario: example2 When name is "Maria" Then greetings equals "Hello Maria" Pros and Cons of Behavior-Driven Development (BDD) BDD is an approach that involves managers, testers, developers, etc., in the whole process. As a result, BDD offers a huge number of benefits. Let’s look at some of the major ones in this section. Advantages of Behavior-Driven Development (BDD) Improved Communication: Creating scenarios requires close coordination between clients, managers, developers, testers, etc. This unifies the team in understanding the product behavior. Reduced cost of Quality Control: Automated acceptance tests are used to depict the scenarios, which in turn helps in reducing the costs involved in inspecting the product quality. Accurate task estimation: Since the expected behavior is predicted before, there are few chances to change the software application’s architecture. Better user experience: The scenarios and tests written before development take the user’s perspective into account. The focus is on the desired behavior rather than on implementing features. Excellent documentation: When a certain test fails, the specification is updated, resulting in detailed documentation. Challenges of Behavior-Driven Development (BDD) Requires more involvement from all the stakeholders: Putting all the people together becomes difficult for teams. Is it the three amigos sitting down together, talking, collaborating, and leaving with a common language around the system requirements? BDD tools struggle with parallelization: Cucumber and SpecFlow do parallelization or support parallel testing in a sub-optimal manner. They parallelize at the feature file level. It implies that if you want to run 50 tests in parallel, you need to have 50 feature files. That’s a lot of feature files. Writing incorrect Gherkin syntax: The issue is that most of us do not follow the correct Gherkin syntax as prescribed by the BDD creators. Just remember Given-When-Then steps must appear in order and cannot repeat. TDD vs. BDD: The Final Showdown TDD vs. BDD is a quest for some developers. Even experienced developers find it difficult to differentiate between these approaches. Now that we have touched upon the working and implementation of TDD and BDD let’s deep dive into the major differences in this epic TDD vs. BDD clash: Criteria Test-Driven Development(TDD) Behavior-Driven Development(BDD) Language Test cases designed in TDD are Technical. These are similar to the test cases that are normally written during the testing phase. The test scenarios designed in BDD are written in simple English language. Implementation Level There is a low-level implementation in TDD. The scenarios are easy to understand and implement, making BDD a high-level implementation with regard to test case development. Key Stages Test case development is the major phase in TDD. Discussion and creation of scenarios are the major stages in BDD. Stages involved in Development TDD involves three main stages, Test creation, implementation, and code refactoring are the major stages in TDD. BDD involves a number of stages like feature discussion, scenario creation, testing, implementation, and code refactoring. Participants Only technical teams like development teams take part in TDD processes. BDD involves many teams, right from client to business analysts, testers, developers, etc. Primary Focus Development of required functionality based on test cases is the primary focus in TDD. BDD focuses on the correspondence between implemented features and expected behavior. Documentation TDD requires documentation for the creation of accurate test cases. Thrust is laid on documentation created during the scenario creation process. Tools The tools (or frameworks) used in TDD involve JUnit, TestNG, NUnit, etc. These are used to run test cases. Gherkin is used for writing scenarios in BDD. Cucumber, SpecFlow, etc., are some of the widely used test automation frameworks. Applicable Domain The main focus in TDD is to get appropriate functionality through implementation. BDD has the defined domain as “Behavior.” This focuses on the product’s behavior at the end of implementing the product functionality. Bug Tracking Bug tracking is easier in TDD, as the tests indicate whether they have passed or failed. Bug tracking in BDD requires integration between multiple tools across the organization. So, these were the key differences as far as TDD vs. BDD is concerned. So, make sure to look at these differences when you have to decide between TDD and BDD. Can TDD and BDD Work Together? So far, we have seen what is different as far as TDD vs. BDD is concerned. The best part is that these processes are not mutually exclusive. While it’s not unusual for Agile teams to use one without the other, making these two work together can ensure a higher degree of efficiency in testing use cases, thereby bringing confidence in the performance. TDD, when used alongside BDD, gives importance to web testing from the developer’s perspective, with greater emphasis laid on the application’s behavior. To implement the specifics, developers can create separate testing units to get robust components. This is beneficial since the same component can be used in different places across the software development process. You can use cloud tools like LambdaTest to leverage the capabilities of both TDD and BDD frameworks and perform live testing. LambdaTest is a cross-browser testing platform that enables you to run your test scripts on an online device farm of 3000+ real browsers and real operating systems. LambdaTest integration with tools like Slack, Microsoft Teams, etc., makes discussions between the teams efficient and easy. TDD or BDD, the choice is governed by the individual needs of an application or the enterprise. The combination of TDD and BDD frameworks can add more value to the software development process. This is where automation testing tools like LambdaTest can be beneficial since they can be integrated with major TDD and BDD frameworks. TDD vs. BDD: Which Approach Is Best for Your Project? BDD and TDD have differences and similarities. Although, at the same time, it’s not unusual for Agile teams to use one without the other, making the two work together can guarantee a higher degree of efficiency in testing application use cases, thus obtaining greater confidence in their performance. TDD vs. BDD, an Agile team, alongside TDD, can use the BDD approach to put in place a higher level of testing that takes care of technical nuances from the Agile team’s point of view and assesses the application’s behavior. To implement the specifics, we can create separate testing units to maintain the robustness of different components. It can be beneficial, considering that the same component can be used in other places across an application. The testing process is based on specifying test scenarios in simple language. Then, Automation Engineers add TDD parts for testing certain specific components. Whether to choose BDD over TDD or use a combination of the two methods is a choice ruled by the needs of an application or organization. Experimenting with BDD if you’re already doing TDD testing can add value to the Agile process. Using the two together is more accessible, and you don’t need to change or rework the existing approach. All it takes is updating the testing framework to adjust the other. Selenium 101 certification from LambdaTest is a great way to validate your expertise in Selenium automation testing. There are plenty of good reasons to get Selenium certified. You can use it to prove that you’re on top of things, or you can use it as a way to help yourself learn. Final Thoughts In this TDD vs. BDD article, you can always view the big picture and decide which approach is best for your software requirements. However, whether you choose to implement test-driven or behavior-driven development, you will require strong QA and testing skills and tools. Understanding TDD vs. BDD approaches work can help Agile teams and other stakeholders improve the development process by zeroing in on the best test strategy for their needs. Then, depending on the nature of the project and the desired result, you can choose between a mix of the two techniques to enhance test coverage efficiency. Thus, till then, Happy Testing!
This is a question that I hear on a fairly regular basis, not just internally but from external customers as well. So it’s one that I would like to help you walk through so that you can really figure out what makes sense in your organization, and I think the answer is probably going to surprise you a little bit. I think probably the most important thing to understand is this isn’t a versus question. You don’t have to have one or the other. As a matter of fact, I would argue, and I think that many people would agree, that SRE is actually an essential component of DevOps, and a good, properly implemented DevOps method leads to the necessity of SRE when it comes to deployment. So there are two sides to the same coin, so that will obviously lead to a little bit of confusion because DevOps is the development methodology; it’s all about integrating your development teams and your operations teams. It’s about knocking down those silos between them. It’s about ensuring that everybody is singing the same songbook, and that’s very important. SRE is in charge of automating all of the things and making sure that you never go down. Two sides of the same coin There are really two parts of the same group, so let’s look at the differences because they do have some differences. Probably the first and largest one is that when we think about our DevOps.The DevOps guys, particularly your developers, are doing the Core Development. They are answering the question, “What do we want to do?” they are working with product, they’re working with sales, they’re working with marketing to develop the design and deploy. What is it that we do? They’re working on the core. On the other hand, SRE is not working on the Core Development. What they are working on is the implementation of the core, they are working on the deployment, and they are constantly giving feedback back to that core development group to say, “Hey, something that you guys have designed isn’t working exactly the way that you think that it is” If you want to think about it this way DevOps is trying to develop. SRE is saying how we deploy and maintain and run to solve this problem. It’s theoretical versus practical. Ideally, they’re talking to each other every day because SRE should be logging defects; they should be logging tickets back with development. Still, probably most importantly, they need to understand that they have the same goals. These groups should never be aligned against one another. And so, they do have to have a common understanding. Let’s see about the most important part; we’re going to talk about failure because failure is not necessarily failure; it’s just a way of life. It doesn’t matter what you deploy. It doesn’t matter how well it goes; it will happen. There is a failure budget or an error budget where things will go wrong. SRE team, when it comes to failure, they’re going to anticipate it, they’re going to monitor it, they’re going to log it, they’re going to record everything, and ideally, they can identify a failure before it happens. They’re going to have predictive analytics that will say, “All right, this thing is going to go bad based on what we’ve seen before.” So, SRE is responsible for mitigating some of those failures through monitoring, logging, and doing the preemptive parts. So we’ll do the monitors, we’ll do the logs. SRE is also going to lead all of your post-actual failure incident management. They’re going to get you through the incident, to begin with, and then they’re going to hot wash it, and when it’s done, you have to get Dev online because these are the guys who are going to solve the core problem; some RCAs might be solved by SRE internally. Then SRE team will integrate the fix into their monitoring and their logging efforts to make sure that we don’t get into another RCA for the same kind of problem. There are different skill sets. Core development DevOps, these are the guys that really love writing software. SRE is a little bit more of an investigative mindset, right? You have to be willing to go and do that analysis, figure out what things have gone wrong, and automate everything. But there’s a lot that they have in common. Everyone should be writing automation; everyone should get rid of toil as much as possible because we just don’t have the time to do manual tasks. When we can put the computers in charge of it, computers are not great at thinking on their own, but if you need it to do the same thing repeatedly, you can’t beat computing for that. And so, automation is key; you have a slightly different mindset. DevOps is going to automate deployment; they’re going to automate tasks; they’re going to automate features. SRE will automate redundancy and manual tasks that they can turn into programmatic tasks to keep the stack up.
Low code development has gained significant momentum in recent years as a trend in software development. According to MarketsandMarkets, the low code development market is expected to achieve a substantial growth rate of 28.1% and reach a value of $45.5 billion by 2025. This significant growth demonstrates the growing demand and adoption of low-code platforms across various industries and businesses of all sizes. But how does low code compare to traditional development, which relies on coding languages and frameworks? Is low code suitable for complex and scalable projects? What are the benefits and drawbacks of each approach? And most importantly, how can you determine which one is best for your needs? If you are interested in learning more about low code vs. traditional development, keep reading this article. I will provide you with an in-depth analysis and comparison of both approaches and help you make an informed decision for your project. Understanding Low Code Development Low code development refers to a visual development approach that empowers you to create applications with minimal manual coding. It provides a graphical interface and pre-built components that allow developers and non-technical users to rapidly build and deploy applications. This approach significantly reduces the time and effort required to create software solutions. The benefits of low code development are manifold. Firstly, it accelerates the application development process. By providing a drag-and-drop interface, reusable components, and predefined templates, low-code platforms enable you to quickly piece together an application's building blocks. This expedites the development lifecycle, allowing for rapid prototyping and faster time-to-market. Secondly, low code development increases efficiency and productivity. With its intuitive visual interface, you can focus on the application's logic and functionality rather than spending excessive time writing code from scratch. This streamlined approach eliminates repetitive coding tasks and allows you to work more efficiently, leading to increased productivity and faster project delivery. Another advantage of low code development is the reduced reliance on coding expertise. Traditional development often requires deep technical knowledge and coding proficiency. In contrast, low code platforms abstract much of the underlying complexity, enabling professionals from various backgrounds, such as business analysts or citizen developers, to actively participate in the application development process. This democratization of development fosters collaboration enhances cross-functional teamwork, and allows for innovation beyond traditional developer roles. Low code development has found success in various domains, including rapid prototyping, internal business applications, customer-facing applications, and process automation. Its ease of use and visual nature makes it particularly appealing for quick iterations, agile development practices, and scenarios where time is of the essence. Traditional Development: The Tried and True Traditional development refers to the conventional approach of building applications through manual coding using programming languages and frameworks. It follows a well-defined software development life cycle (SDLC) that typically includes phases such as requirements gathering, design, coding, testing, and deployment. One of the key advantages of traditional development lies in its flexibility and customization options. You have complete control over every aspect of the application, from the architecture and design to the underlying code. This level of control allows for highly tailored solutions that meet unique requirements, complex business logic, or specific industry standards. Moreover, traditional development provides a wide range of programming languages and frameworks to choose from, each with its own strengths and specialties. Whether it's the versatility of Java, the speed and performance of C++, or the simplicity of Python, you can select the most suitable tools for your specific project needs. This flexibility empowers you to leverage the power of established ecosystems and tap into a vast pool of libraries, frameworks, and community support. Additionally, traditional development methodologies are well-suited for handling complex and unique requirements. Projects that demand intricate algorithms, extensive data processing, or real-time systems often require fine-grained control and optimization. Traditional development methodologies provide the depth and granularity necessary to tackle such challenges, enabling you to build robust, high-performance solutions. It's worth noting that traditional development methodologies are deeply ingrained in the industry and have a long history of success. Many large-scale enterprise applications, mission-critical systems, and complex software solutions have been developed using traditional approaches. The reliability, predictability, and proven track record of these methodologies make them a preferred choice in certain scenarios. Exploring the Low Code Landscape Several vendors offer low-code platforms, each with its own unique features and strengths. One of the most popular low-code platforms is Microsoft Power Apps. It offers a drag-and-drop interface, pre-built connectors, and an extensive library of templates and components. Power Apps can be used to build a wide range of applications, including internal business solutions, customer-facing apps, and process automation workflows. The platform integrates seamlessly with Microsoft's ecosystem, enabling users to leverage existing data sources and services. Another popular platform is Mendix, which provides a comprehensive low-code development environment for building enterprise-grade applications. The platform offers a visual development interface, model-driven development, and a wealth of reusable components and templates. Mendix also provides a wide range of deployment options, including cloud, on-premise, and hybrid. Salesforce's low code platform, Salesforce Lightning, offers a powerful set of tools for building custom applications on the Salesforce platform. Lightning provides a drag-and-drop interface, pre-built components, and an extensive set of APIs for integrating with external systems. The platform also includes AI-powered automation capabilities and robust reporting and analytics features. Low code development has rapidly gained traction in the software development industry due to its ease of use, rapid prototyping capabilities, and democratization of development. Traditional Development: Delving Into the Details When it comes to programming languages, traditional development offers a vast array of options, each with its own strengths and purposes. For instance, Java is a versatile language known for its scalability, platform independence, and extensive libraries. It's commonly used in enterprise applications and systems that require high performance and reliability. Other popular languages include C++, which is known for its efficiency and low-level programming capabilities, and Python, renowned for its simplicity and readability. Languages such as JavaScript and C# also find widespread use in web and desktop application development, respectively. The choice of programming language depends on factors such as project requirements, performance needs, and developer expertise. Frameworks play a crucial role in traditional development by providing developers with pre-built components, libraries, and best practices. These frameworks help streamline the development process and promote code reuse. Examples of popular frameworks include Ruby on Rails, Django, .NET, and Laravel. Each framework offers its own set of features, conventions, and benefits, catering to different development needs and preferences. In traditional development, a team composition typically consists of different roles and expertise to ensure smooth collaboration and efficient development. Some common roles include: 1. Project Manager: Responsible for overall project planning, coordination, and stakeholder management. 2. Business Analyst: Gathers requirements, analyzes business processes, and translates them into technical specifications. 3. Software Architect: Designs the overall structure and architecture of the application, ensuring scalability and maintainability. 4. Developers: Responsible for writing the code and implementing the functionality based on the design and specifications. 5. Quality Assurance (QA) Engineers: Conduct testing, identify bugs, and ensure the software meets quality standards. 6. DevOps Engineers: Handle deployment, infrastructure management, and automate software delivery processes. 7. Technical Writers: Create documentation and user guides to aid in software understanding and usage. The team composition may vary depending on the project size, complexity, and organizational structure. Collaboration and effective communication among team members is essential to ensure successful traditional development projects. Low Code vs. Traditional Development The following table summarizes the key differences between low code and traditional development: Aspect Low Code Development Traditional Development Cost Analysis Lower initial costs, no extensive coding needed, cost-effective pricing models. Higher initial costs, skilled developers required, and custom solutions add to the cost. Performance and Scalability Slightly lower performance, improving with advancements. High performance, scalable for complex projects (depending on the development team's skills). Security and Compliance Standardized security features and limited customization. Custom security implementations, suitable for stringent requirements. User Experience and Design Visual interfaces, drag-and-drop functionality, rapid design iterations. Complete design freedom and highly tailored experiences. Collaboration and Teamwork Enables citizen developers, efficient communication, and rapid iteration cycles. Relies on skilled developers and the technical expertise required. Integration and Interoperability Pre-built connectors and simplified integration. Custom integration mechanisms are suitable for complex requirements. Maintenance and Upgrades Automated maintenance and upgrades. Dedicated resources, version control, and regular updates. Vendor Lock-in and Long-Term Viability Some platform dependencies assess the long-term viability. Independence, technology,y and infrastructure choices, reduced risk of vendor lock-in. Use Cases Rapid prototyping, internal tools, citizen development. Complex custom solutions, regulated industries. In the upcoming sections, we will dive deeper into each aspect in more detail. Cost Analysis In this section, we'll delve into the cost considerations associated with low code and traditional development approaches. Low code development often offers cost advantages in terms of reduced development time and resources. The visual, drag-and-drop nature of low-code platforms allows for faster prototyping and development cycles, potentially resulting in lower labor costs. Moreover, low code development minimizes the need for extensive coding expertise, reducing the cost of hiring specialized developers. Additionally, low-code platforms often provide pre-built components, templates, and integrations, saving development time and effort. This can result in shorter time-to-market and cost savings, especially for applications with standard or common requirements. Furthermore, the ease of use and visual development interface of low-code platforms can enable citizen developers or business users to participate in the development process, reducing the need for a large development team. On the other hand, traditional development may involve higher upfront costs due to the need for specialized development resources, including experienced developers and architects. The customization and fine-grained control offered by traditional development methodologies often require skilled professionals who command higher salaries or hourly rates. Additionally, the longer development cycles and extensive testing involved in traditional development can contribute to increased costs. However, traditional development also offers cost advantages in certain scenarios. For complex projects with unique requirements or specialized functionalities, traditional development allows for more tailored solutions. This can potentially lead to long-term cost savings by avoiding the limitations or additional costs associated with customizations on low-code platforms. Maintenance costs should also be considered when analyzing the overall cost of each approach. Low code development platforms often provide updates, bug fixes, and security patches as part of their subscription plans, reducing the burden on development teams. Traditional development, on the other hand, requires dedicated resources for ongoing maintenance, updates, and bug fixes, which can contribute to higher long-term costs. Performance and Scalability Performance is a critical consideration for any software application. It refers to how well an application performs in terms of speed, responsiveness, and resource utilization. In traditional development, you have fine-grained control over the code and can optimize it to achieve high performance. They can implement custom algorithms, optimize data structures, and fine-tune the application's behavior to maximize efficiency. This level of control allows traditional development to excel in scenarios that require complex computations, heavy data processing, or real-time systems. Low code development, on the other hand, abstracts much of the underlying code and focuses on rapid development and ease of use. While low-code platforms handle performance optimizations behind the scenes, they may have limitations in certain areas. For applications with extensive computational needs or performance-critical requirements, low code development may not offer the same level of fine-tuning as traditional development. When it comes to scalability, both low code and traditional development approaches have their considerations. Scalability refers to the ability of an application to handle increased workload, user traffic, and data volume without compromising performance. In traditional development, scalability is often achieved through careful design, architecture, and the use of scalable infrastructure. You can design systems to handle high traffic, distribute the workload across multiple servers, and leverage technologies like load balancing and caching. This level of control allows traditional development to scale horizontally and vertically based on the needs of the application. Low code development platforms often provide scalability features out of the box, such as automatic scaling and cloud deployment options. These platforms leverage the underlying infrastructure to handle increased demand and ensure the application can handle growing user bases. However, the level of control over scalability may be more limited compared to traditional development. If you have unique performance needs, complex algorithms, or real-time processing requirements, traditional development may offer the flexibility and control necessary to optimize performance. On the other hand, if speed to market and rapid application development are priorities, low code development can provide a viable solution with built-in scalability features. Security and Compliance Security is of paramount importance for any software application, as it ensures the protection of sensitive data, prevents unauthorized access, and safeguards against potential vulnerabilities. Traditional development provides you with fine-grained control over the code, allowing you to implement robust security measures. You can apply industry-standard encryption algorithms, handle user authentication and authorization, and implement secure coding practices to mitigate common vulnerabilities. With careful attention to security practices, traditional development can provide a high level of customization and control over security aspects. On the other hand, low-code development platforms typically have security features built-in, ensuring a baseline level of security for applications developed on their platforms. These platforms often incorporate security best practices, such as user authentication mechanisms, data encryption, and protection against common vulnerabilities. However, the level of customization and control over security measures may be more limited compared to traditional development. Compliance with industry regulations and standards is another critical aspect of software development. Different industries have specific compliance requirements, such as HIPAA for healthcare, PCI DSS for payment card processing, and GDPR for data privacy. Both low code and traditional development approaches can address compliance, albeit with different considerations. Traditional development allows for fine-grained control over compliance requirements. You can implement specific controls, conduct thorough testing, and ensure compliance with industry standards. The flexibility and customization options of traditional development can make it easier to address industry-specific compliance needs. Low code development platforms often provide compliance features and tools to help you meet regulatory requirements. These platforms may offer built-in compliance templates, data handling controls, and audit trail capabilities. However, it's essential to ensure that the low-code platform you choose aligns with the specific compliance standards applicable to your industry. In both low code and traditional development, ensuring security and compliance requires a comprehensive approach. It involves not only the development process but also ongoing monitoring, vulnerability assessments, and timely updates to address emerging threats or regulatory changes. Regular security audits, penetration testing, and adherence to secure coding practices are essential regardless of the development approach chosen. User Experience and Design User experience plays a crucial role in the success of any software application. It encompasses the overall satisfaction and usability that users experience while interacting with the application. Low code development platforms often provide a range of pre-built user interface (UI) components, templates, and design elements. These tools can expedite the development process by enabling you to create visually appealing interfaces without extensive design expertise. The drag-and-drop interfaces and intuitive workflows of low-code platforms can also contribute to a positive user experience, especially for applications with straightforward requirements. However, the ease of use and pre-built nature of low-code platforms may result in less flexibility and customization when it comes to design. While these platforms offer a wide variety of design options, they may not provide the same level of freedom to create highly customized or unique interfaces compared to traditional development. Traditional development approaches allow for more granular control over the user interface design. You can leverage UI frameworks, design patterns, and custom styling to create highly tailored and visually stunning interfaces. The ability to craft pixel-perfect designs and incorporate complex interactions can result in a highly polished user experience. Moreover, traditional development enables designers and developers to closely collaborate, iterating on design concepts and incorporating user feedback throughout the development process. This iterative approach can lead to a more refined and user-centric design, aligning closely with the target audience's needs and preferences. Collaboration and Teamwork In this section, we'll explore the aspects of collaboration and teamwork in the context of low code and traditional development approaches. Low code development platforms often provide visual interfaces and simplified workflows that enable business users, citizen developers, and IT professionals to collaborate more seamlessly. The intuitive nature of low-code platforms allows for easier communication and understanding between non-technical stakeholders and developers. This can facilitate a more collaborative environment where stakeholders can actively participate in the development process, provide feedback, and suggest improvements. Moreover, low-code platforms often offer features for collaborative development, such as version control, real-time collaboration, and shared repositories. These features enhance team collaboration by enabling multiple team members to work simultaneously on different aspects of the application. This can result in faster development cycles, reduced dependencies, and improved overall productivity. Traditional development approaches also emphasize collaboration and teamwork. The well-defined roles and responsibilities within a traditional development team foster effective communication and coordination. Each team member contributes their expertise to the project, ensuring that different aspects, such as requirements gathering, design, development, and testing, are handled efficiently. Traditional development methodologies often involve practices such as code reviews, regular team meetings, and collaborative problem-solving sessions. These practices promote knowledge sharing, cross-functional collaboration, and the identification of potential issues or bottlenecks early in the development process. Effective collaboration within traditional development teams can lead to a cohesive and well-coordinated effort toward building high-quality software solutions. It's important to strike a balance between collaboration and control in both low-code and traditional development approaches. While low code development encourages collaboration with non-technical stakeholders, it's crucial to ensure proper governance, security, and quality control measures are in place. Similarly, traditional development teams should foster open communication channels and embrace agile practices to promote collaboration while maintaining project timelines and quality standards. Integration and Interoperability In this section, we'll explore the aspects of integration and interoperability in the context of low code and traditional development approaches. Integration refers to the ability of software systems to work together, share data, and communicate seamlessly. Interoperability, on the other hand, focuses on the broader compatibility between different systems or technologies. Both low code and traditional development approaches have considerations when it comes to integration and interoperability. Integration Low Code Development: Low code development platforms often provide built-in integrations and connectors that enable easy integration with popular systems and services. These platforms may offer pre-built connectors for databases, APIs, third-party services, and enterprise systems, simplifying the integration process. However, the level of customization and flexibility in integrations may vary among low-code platforms. While they excel at integrating with common systems, they may require additional effort for complex or niche integrations. In such cases, custom coding or extending the platform's capabilities might be necessary. Traditional Development: Traditional development allows for extensive customization and control over integration processes. You can use various integration techniques, such as APIs, message queues, and data synchronization mechanisms, to connect different systems. Traditional development methodologies provide the flexibility to tailor integrations to specific requirements. However, this also means that you need to invest time and effort in designing, implementing, and maintaining integrations. Depending on the complexity and scale of the integration, additional expertise or specialized tools may be required. Interoperability Low Code Development: Low code platforms typically offer a standardized environment that promotes interoperability. They often adhere to industry standards, such as RESTful APIs or JSON data formats, making it easier to exchange data with external systems. This interoperability facilitates seamless collaboration between low-code applications and other software components. However, it's essential to ensure that the low-code platform supports the necessary integration protocols or standards required for interoperability with specific systems or technologies. Traditional Development: Traditional development provides you with the flexibility to implement custom interoperability solutions based on project requirements. You can leverage various protocols, data formats, and communication standards to enable seamless integration with external systems or technologies. Traditional development methodologies allow for deeper integration and interoperability options, as you have greater control over the implementation details. This can be advantageous for projects that involve intricate interoperability requirements or legacy system integration. Maintenance and Upgrades Maintenance involves the ongoing support, bug fixes, updates, and enhancements required to keep a software application running smoothly. Upgrades, on the other hand, refer to the process of transitioning to newer versions or technologies. Maintenance Low Code Development: Low code development platforms often provide built-in maintenance and support features as part of their subscription plans. This includes bug fixes, security patches, and updates to the platform itself. As a result, the burden of maintaining the platform and infrastructure is often taken care of by the platform provider. Additionally, the visual and declarative nature of low code development can make it easier to identify and resolve issues, as well as make modifications or enhancements to the application without extensive coding efforts. This streamlined maintenance process can result in shorter downtime and faster resolution of issues. Traditional Development: Traditional development projects require dedicated resources and processes for ongoing maintenance. Development teams need to allocate time and effort to address bug fixes, security vulnerabilities, and software updates. Maintenance activities typically involve code reviews, testing, and ensuring compatibility with new hardware, operating systems, or dependencies. While traditional development allows for full control over the maintenance process, it also means that the development team is responsible for the entire maintenance lifecycle, including infrastructure management and performance optimization. Upgrades Low Code Development: Low code platforms often handle upgrades transparently. When new versions or features are released, the platform provider ensures a smooth transition for their users. This reduces the effort required from the development team to upgrade the underlying infrastructure or platform components. However, it's important to consider the impact of upgrades on existing applications built on the low-code platform. Compatibility issues or changes in the platform's behavior may require adjustments or modifications to ensure a seamless transition. Traditional Development: Upgrades in traditional development require careful planning and execution. Moving to newer versions of programming languages, frameworks, or libraries can involve code refactoring, compatibility testing, and potential modifications to ensure the application functions correctly with the upgraded components. The upgrade process requires expertise and thorough testing to minimize disruptions or regressions. Vendor Lock-in and Long-Term Viability Vendor lock-in refers to the degree to which a development approach ties you to a specific platform or vendor, potentially limiting your flexibility and options in the future. Long-term viability considers the sustainability and longevity of the chosen development approach. Vendor Link-in Low Code Development: Low code development platforms may introduce a certain level of vendor lock-in. When building applications on a specific low-code platform, you become reliant on that platform's ecosystem, proprietary tools, and infrastructure. Transitioning away from the platform or migrating to another vendor may require significant effort and resources. It's essential to consider factors such as data portability, the availability of export capabilities, and the openness of the platform to integrate with other systems. Evaluating the vendor's track record, customer support, and their commitment to ongoing platform development and updates can help mitigate potential vendor lock-in concerns. Traditional Development: Traditional development approaches generally offer more flexibility and independence from specific vendors or platforms. By using industry-standard languages, frameworks, and tools, you have the freedom to choose different vendors or transition to alternative solutions without major rework or disruptions. However, it's important to consider dependencies on specific technologies or libraries, as well as proprietary components, that may introduce some level of vendor lock-in. Evaluating the long-term community support, the popularity of the technologies used, and the presence of active developer communities can help assess the risk of vendor lock-in. Long-Term Viability Low Code Development: The long-term viability of low code development depends on the stability and growth of the platform provider. Assessing the vendor's financial health, market presence, and the rate of platform enhancements and updates can give insights into their commitment to long-term viability. Additionally, considering the extensibility and scalability of the low code platform, as well as its compatibility with emerging technologies, can help ensure its suitability for future needs. Traditional Development: Traditional development approaches benefit from the vast ecosystem of open-source technologies, well-established programming languages, and frameworks. These factors contribute to their long-term viability and sustainability. The availability of developer talent, the size of the developer community, and the active support and development of the technologies used are important indicators of long-term viability. When considering vendor lock-in and long-term viability, it's crucial to balance the advantages of a specific development approach with the potential risks associated with dependencies on proprietary tools or platforms. Use Cases In this section, we'll explore various use cases that highlight the application of both low-code and traditional development approaches. Examining these use cases can provide insights into how each approach is utilized in different scenarios. Low Code Use Cases Rapid Prototyping: Low code development is ideal for quickly prototyping and validating ideas. It allows businesses to build functional prototypes without investing extensive time and resources. Internal Business Tools: Low-code platforms enable non-technical users to create internal tools, such as data entry forms, workflow automation, and reporting dashboards, improving operational efficiency. Citizen Development: Low code development empowers citizen developers, who have domain expertise but limited coding skills, to build their own applications, reducing dependency on IT departments. Mobile App Development: Low-code platforms often provide mobile app development capabilities, allowing businesses to create cross-platform mobile applications with minimal coding effort. Traditional Development Use Cases Complex Enterprise Solutions: Traditional development is well-suited for building complex enterprise solutions that require extensive customization, integration with existing systems, and scalability. Custom Software Products: Traditional development allows for the creation of custom software products tailored to specific industry requirements or niche markets. Performance-Critical Applications: Applications that demand high performance, such as financial systems, real-time data processing, or scientific simulations, often require the fine-tuning and optimization capabilities of traditional development. Legacy System Modernization: Traditional development is often used to modernize legacy systems by migrating them to modern architectures or technologies while retaining critical functionality. Future Trends and Predictions The field of software development is constantly evolving, and staying aware of emerging trends can help inform decision-making and shape development strategies. Low Code Development Trends Continued Growth: The popularity of low code development is expected to grow further as businesses seek ways to accelerate application development and empower citizen developers. AI and Automation Integration: Low code platforms are likely to incorporate artificial intelligence (AI) and automation capabilities, enabling intelligent automation of routine tasks and enhancing application intelligence. Industry-Specific Solutions: Low-code platforms are expected to offer industry-specific templates, pre-built modules, and solution accelerators to cater to specific sectors, such as healthcare, finance, and retail. Integration with Emerging Technologies: Low code development will likely embrace emerging technologies like machine learning, blockchain, and the Internet of Things (IoT), allowing you to build advanced applications with ease. Traditional Development Trends Microservices and Containerization: Traditional development approaches are anticipated to leverage microservices architecture and containerization technologies for building scalable, modular, and portable applications. Cloud-Native Development: With the increasing adoption of cloud computing, traditional development will focus on building cloud-native applications that take full advantage of cloud services, scalability, and resilience. DevOps and Agile Practices: Traditional development methodologies will continue to embrace DevOps and agile practices, enabling rapid development, continuous integration, and deployment for faster time-to-market. Security and Privacy Focus: Traditional development will place a greater emphasis on incorporating robust security measures and privacy considerations into the development process, addressing the evolving threat landscape. The future may witness a convergence of low code and traditional development approaches. Low code platforms might evolve to offer more advanced coding capabilities, while traditional development practices might adopt visual and declarative elements to enhance developer productivity. This convergence could lead to a hybrid approach that combines the strengths of both approaches. Making the Right Choice Here are some key factors to consider when choosing between low code vs. traditional development: 1. Project Complexity and Scale: Evaluate the complexity and scale of your project. Low code development is well-suited for smaller to medium-sized projects with less complex requirements. Traditional development provides greater flexibility for large-scale, complex projects that require custom solutions and extensive control. 2. Development Speed and Time-to-Market: Assess the urgency and time constraints of your project. Low code development offers rapid application development capabilities, enabling faster time-to-market. Traditional development may take longer due to the need for coding from scratch, but it offers more customization options. 3. Developer Skillset and Resources: Consider the skillset and resources available within your development team. Low code development platforms empower citizen developers and those with minimal coding experience to participate in the development process. Traditional development requires more coding expertise and may require a dedicated team of skilled developers. 4. Long-Term Maintenance and Upgrades: Evaluate the long-term maintenance and upgrade requirements of your project. Low code development platforms often handle maintenance and upgrades transparently, while traditional development requires dedicated resources and processes for ongoing support and upgrades. 5. Integration and Interoperability Needs: Consider the integration and interoperability requirements of your project. Low-code platforms often provide pre-built integrations and connectors, simplifying the integration process. Traditional development allows for more customization and control over integration mechanisms. 6. Cost Considerations: Assess the budget and cost implications. Low code development may offer cost savings by reducing development time and the need for extensive coding expertise. Traditional development can provide cost savings in the long run for large-scale projects that require customizations. 7. Vendor Lock-in and Long-Term Viability: Evaluate the level of vendor lock-in and the long-term viability of the chosen development approach. Consider factors such as data portability, the availability of export capabilities, and the vendor's track record and commitment to ongoing development and updates. Conclusion In this comprehensive comparison of low code and traditional development, we have explored the key aspects, advantages, and considerations associated with each approach. Both approaches have their strengths and are suitable for different types of projects and requirements. Low code development offers rapid application development, visual interfaces, and the empowerment of citizen developers. It enables faster time-to-market, reduces the reliance on extensive coding expertise, and provides built-in features for maintenance and upgrades. Low code is particularly effective for smaller to medium-sized projects with less complex requirements and tight timeframes. On the other hand, traditional development provides full control, customization options, and scalability for large-scale, complex projects. It requires coding expertise, extensive planning, and dedicated resources for maintenance and upgrades. Traditional development offers flexibility, compatibility with diverse technologies, and the ability to build highly customized solutions. I hope this comparison has provided valuable insights and guidance in choosing the right development approach for your projects. Thanks for reading! FAQs Here are answers to some frequently asked questions related to low code and traditional development: Q: Can low code development replace traditional development entirely? A: Low code development can be suitable for certain projects, but traditional development may still be necessary for complex, highly customized solutions. Q: Which approach is more cost-effective in the long run: low code or traditional development? A: Cost-effectiveness depends on project complexity and scale. Low code development can be cost-effective for smaller projects, while traditional development may offer long-term cost savings for large-scale projects. Q: How does the performance of low-code applications compare to traditionally developed applications? A: Low-code applications may have slightly lower performance due to the abstraction layer, but advancements in low-code platforms are improving performance. Q: What are the security considerations when choosing between low code and traditional development? A: Both approaches require proper security measures, but traditional development provides more control over security implementations. Q: Can low code and traditional development be used together in a project? A: Yes, hybrid approaches that combine low code and traditional development techniques can be used to leverage the benefits of both approaches. Q: Are there industry-specific use cases where low code or traditional development is more suitable? A: Low code development is often used for rapid prototyping, internal tools, and citizen development. Traditional development is preferred for highly regulated industries and complex custom solutions. Q: How do collaboration and teamwork differ between low code and traditional development? A: Low code development encourages collaboration between developers and non-technical stakeholders, while traditional development relies more on technical expertise and specialized roles. Q: What are the key factors to consider when choosing a low-code platform or a technology stack for traditional development? A: Factors include project requirements, scalability, customization needs, integration capabilities, and long-term vendor support. Q: What are the emerging trends and future predictions for low code and traditional development? A: Low code development is expected to grow further, incorporating AI, industry-specific solutions, and integration with emerging technologies. Traditional development will focus on microservices, cloud-native development, and security enhancements.
AI's impact on Agile Project Management and Scrum Mastery will go from "interesting" to "total game-changer" faster than you think. My team and I have spent years at the intersection between AI and software creation. As a result, we have some fascinating conversations with product managers, product owners and project managers, Scrum masters, and the like. Probably people like you. So I wanted to write about the direction in which AI is taking agile, scrum, and project management. Excellent AI is still very green. Not all this tech is ready, but I will stick my neck out and say it will be in the next six months. TL;DR: Don't leave it until it's too late to explore how to integrate AI safely. Agile Planning Your development team is in the middle of a crucial sprint, and suddenly, an unforeseen issue arises, disrupting the entire project timeline. In tech, such hiccups can cost you dearly in terms of time and resources. Plus, you must figure out how to explain this to management and potentially your customer. But what if AI could help you anticipate and mitigate potential challenges before they even occur? Enter AI-powered predictive analytics. By tapping into historical data and employing advanced machine learning algorithms, predictive AI solutions can analyze patterns, identify trends, and forecast potential obstacles in your project's path. Let me give some examples. Estimations: Human estimates are flawed by nature. We're just not wired to do it. AI will enable realistic sprint planning, release planning, and better resource allocation. Risks: AI will be able to spot risks and bottlenecks far more consistently and – on average – faster than humans can. That means you can mitigate them before they cause problems. Prioritization: AI-powered analytics will be able to prioritize and adaptively reprioritize your product backlog efficiently. There will be far fewer overheads to this process when driven by AI, and it'll spot dependencies and keep everybody strategically aligned on what matters automatically. Collaboration The backbone of any successful Agile team lies in collaboration and effective communication. But keeping everyone on the same page is a huge time drain. Miscommunication (and its consequences) is among the most-mentioned frustrations of the PMs I speak to. That grows exponentially as the complexity (of projects and teams) increases. And that is not to mention the hours out of every day that engineers and PMs spend catching up on Slack or Teams, fishing through old messages to find resources, or working out what work has been done on other areas of the project. That time spent on information-seeking is, for most teams, necessary. But I think AI will turn that "time spent" into "time wasted." Let me illustrate: No more trawling. AI will be able to understand everything happening on every project you're working on and surface the important information from the tools you use, like Jira, Slack, Teams, and GitHub. All-knowing AI. LLMs are now more than good enough to allow you to ask any question you like about project progress, risks, or the like and give you a concise, actionable answer. Fewer, better meetings. For one thing, there should be no need in an AI world to spend time in meetings on progress updates or summarizing data. Instead, meetings will be more strategic and creative. I don't know many people in software who wouldn't leap at this one. Continuous improvement Continuous improvement is inherent to the agile methodology, the agile manifesto. It's all about enhancing your team's efficiency, productivity, and effectiveness with each sprint. I think AI represents an opportunity for a significant shift – or "step up," if you like – in how continuous improvement happens. Let's look at what this could look like for your team. 1. Quality: It's already possible to support processes like code review and deployment with AI, and the development process itself has a wealth of tools available. 2. Performance insights: AI is already available to help you understand your team's performance, identify patterns, and make data-driven decisions to improve your processes. It will be far more adept than humans at everything from high-level insights to highly granular and specific insights. Use them to pinpoint areas for improvement. In addition, it’s real-time and has almost no time overheads, which speeds the whole thing up and means the agile planning process can be far more dynamic. 3. Resource allocation: Make sure everyone is working on the tasks that align with their skills and strengths – or even their opportunity for growth. It's a win-win. You boost productivity, and your foster a more supportive culture. What Next? Let's turn down the hype for a moment. Right now, embracing AI to overhaul traditional project management and Scrum practices isn't an absolute must-have. After all, much of the tech is very green, with many AI tools in Beta or still using old underlying models (like GPT-3, which is fine but isn't going to change the world.) So you’re probably not losing significant ground over your competitors. However, This clock is ticking faster than any figurative time bomb I can remember. It will be a matter of months, not years, before at least partial adoption of AI software development tools is no longer a luxury but a necessity. Adopting and integrating the right tools safely will be the greatest challenge for team members who make decisions about tools for the agile cycle. It's pretty much a full-time job to keep up with advancements in AI.
A shift left testing approach involves moving testing activities "left" or relatively "early" in the development cycle. Thus, testers are involved earlier in the software development life cycle, enabling them to identify bugs and bottlenecks at an earlier stage. In addition to improving the quality of the code and reducing the time it takes to complete the cycle, it helps ensure fewer defects are introduced to production. Organizations are constantly challenged to move faster in an agile environment. Typically, this entails shortening the delivery time while improving quality with each successive release at reduced costs. Agile development initiatives have prioritized short sprints and planned to incorporate customer feedback into features as promptly as possible. However, such advancements encounter severe quality issues unpleasing to customers. Following this, the drift in testing and quality has emerged, where "moving fast and break-in things" is no longer sustainable, and testing has become a significant stumbling block. In traditional models, testers usually start testing after development. In response to this, a quality assurance technique known as shift left testing has emerged. It offers advantages such as cost savings and bug detection upfront. What Is Shift Left Testing? The shift left approach urges us to rethink how to approach software quality improvement. Earlier, in a waterfall methodology, developers and quality assurance experts worked in a team with distinct roles and responsibilities. Even in agile methods, testing usually comes last. The idea of shift left testing intends to bring the testing stage much earlier in the software development life cycle (SDLC). Therefore, teams can work together more effectively and collaboratively and communicate regularly with one another. Thus shifting left is a common term for early bringing together testing and development. Let's consider a scenario where everything is completed on the developers' side. They hand over to the testing team, to begin with system testing, and the development team starts on new projects. During QA testing, tests come up with bugs that must be fixed before they can be released to production. In this case, the developers again stand by the current task and concentrate on fixing bugs in the last project to mitigate deadlines, or the deployment would have to wait until a new release cycle. Talk about this extra investment of time! The shift left approach aims to improve quality by moving tasks to the left as early as the life cycle. It incorporates testing earlier in the software development life cycle by repeatedly testing and detecting issues and minimizing the risks early. Therefore, a shift left in testing might be a more realistic way of detecting bugs for products compared to typical testing approaches, which start testing at the end of development. Impact of Testing Late in the SDLC Defects are more complex and expensive to fix that are found later in the life cycle. This is why treating testing as a sequential phase at the end of the waterfall approach has been regarded as a major pitfall of software testing. Following are the impacts of testing late in the development cycle. Testers may be less engaged in initial planning, resulting in inadequate testing resources being allocated. Many requirements, architecture, and design flaws are not discovered and rectified until a considerable effort has been spent on their implementation. As more software is created and integrated, debugging becomes more difficult. Less time for test automation, which eventually leads to regression defects. Encapsulation makes white box testing and achieving high levels of code coverage during testing more difficult. There is less time to fix defects discovered during testing, increasing the likelihood of being postponed in the later system upgrades. Late testing impacts development and maintenance costs, leads to missed deadlines and project delays, and lowers quality due to residual defects. Customers get a bad end-user experience due to buggy software. Why Shift Left Testing? With a traditional waterfall model, where testing occurs at the end of the cycle, severe defects are often missed. Such critical bugs are difficult and costly to fix at the end of the process. As bugs are discovered, the cost of fixing them rises exponentially. However, shifting left testing by involving testers early in the cycle helps to reduce costs associated with bug discovery and fixes. As a result, there is no delay or impact on the deliverables of the project, and it improves customer satisfaction. Here are some appealing factors why we see a rapid drift towards the shift left approach. By joining coding and testing, the shift-left methodology lessens code insecurities. Also, this builds engineers' productivity. The shift left methodology empowers engineers to rapidly test code through ceaseless coordination (CI) and test automation. This permits groups to develop their SDLC toward constant testing and the CI/CD pipeline. Discover bugs almost immediately in the product advancement life cycle. Cut down the expense of addressing bugs by recognizing them almost immediately. Discover bugs almost immediately in the product advancement life cycle. Shift-left testing in DevOps brings about a more significant item. This builds consumer loyalty and improves business results. Gain a more significant item as the code contains fewer fixes and code fixes. Have fewer possibilities that the item overshoots the assessed course of events. Give higher consumer loyalty as the code is steady and conveyed inside the financial plan. Keep up the greater codebase. By testing before the cycle, groups can discover costly deformities sooner. This sets aside time and cash. The shift left approach permits groups to design the whole testing extension even more likely for their ventures. Benefits of Shift Left in Testing Shift left does more than find bugs earlier. It helps the team collaborate better with all stakeholders, improve collective competency, and craft more realistic test cases to ensure defect-free delivery. Shift-left testing brings several cultural benefits since it emphasizes areas of the agile manifesto's well-known principles. Responding to change over following a plan. Customer collaboration over contract negotiation. Working software over comprehensive documentation. Interactions and individuals over processes and test tools. The following are a few significant advantages of shifting left testing. Improved customer and user experience: Shift left promotes high-quality code and on-time delivery with lesser bug fixes, creating a more intensive customer requirements-focused environment that leads to better product design and user experience. Early bug detection: Early, progressive, continuous testing reduces the number of defects before they cause issues in production. Enhanced coverage: The additional advantage of shift left tests is increased test coverage. Tests will evaluate a more significant percentage of your software if more people create tests more frequently and start earlier. Significant cost savings: Early detection considerably results in increased efficiency and reduced technical debt, reducing project costs in terms of effort, time, and cost involved in bug fixes. Improved software team culture, competency, morale, and employee retention: Getting everyone on the delivery team involved in testing activities requires shifting to the left. Your team no longer discusses and performs testing as a last-minute rush just before release. Helps reshape product development: Shift left in testing doesn't mean that we are not testing in production or that testing is completely shifted to the design phase of the software development life cycle. Shift left injects testing into each sprint. Some testing should still occur at the end but should be residual. What Happens When You Shift Left? In the shift left approach, the testers learn first-hand about the ultimate standard for testing the release. For example, testers may realize that it's much more efficient to work closely with component and system developers as they learn about the product specifications. They can ask probing questions, meet with the API developers and work to create test stubs for new services. As testers are actively involved in these earlier phases, they are eventually "shifting left." Let us look at what happens when you incorporate a shift left approach in testing. Design Phase Traditionally, the product design team would wait until they had a critical mass of new features and start a long process of designing them. Until then, testers might have been aloof from the fact that the new features were underway. In modern software development, we can test new feature ideas immediately by bringing in the questions below. What is the purpose of the feature? What problems does it solve for our customers? How will we know this feature is successfully meeting customer needs? What could be the smallest slice of the feature that can be built and used as a "learning release" to ensure it delivers value? What is the worst and best thing to happen when people use this feature? Using Tests Continually to Guide Development Here's a typical "shift left" scenario: Your team has decided to build a feature, and you're having a specification discussion in the planning session with the delivery team. Just asking, "How will we test this?" leads to a productive discussion because it helps you understand the feature and might lead to implementing tests for those stories at the unit, integration, system level/API, UI, or other levels as applicable. Designing Test Plans Rather than formal test plans, the team can capture a couple of instances of desired and undesired behavior for every story as they are created. We can turn those into tests. The team can run that and help with the development. These tests become a detailed documentation of how the feature works once it is in production, and automated regression tests can ensure that no future changes will break it. One way to increase your team's chances of delivering precisely what your customers want is to think of more test cases to automate or manually explore as each feature is built. Test Early and Often, Automate Early and Often The perk of agile development is that we can automate as you go. You can write the most fundamental test for a capability even if we have yet to develop it fully. You can add incremental tests to the production and test codes for this capability. There needs to be more rework, more knowledge of how to write maintainable tests, and more waiting for questions to be answered when testers, developers, and other team members collaborate to automate tests as coding progresses—quite a deviation from waterfall processes' handoffs. It's Not Just About Testers The shift left approach is a technique in which the entire software team is involved. Here are some adaptations that each role needs to make to support this: Testers: Participate actively in the design phase. To test a build, you shouldn't wait for the code. Try to separate client stories into little testable pieces to guarantee that tests are successful and significant. Developers: Participate in discussions about test strategy and planning. Your input may help alleviate the testing team's burden and lower your technical debt. Maintain your automated test suites as an essential component of the code base. Product managers: Make sure your teams have access to the right resources to work together and receive feedback. Set objectives and expectations that are compatible with the shift left strategy. This might entail setting quality objectives for the entire team and anticipating less feature work per sprint. Infrastructure and deployment teams: Your deployment pipeline needs to be available early and have a high capacity so test suites can run promptly because testing needs to happen frequently and early. Types of Shift Left Testing There are four different types of shift left approaches, each providing different values. Traditional Approach Incremental Approach Agile/DevOps Approach Model-based Approach 1. Traditional Approach To understand the traditional shift left approach, it's important to first understand the traditional V-Model in a software development life cycle. The SDLC V-Model is an extension of the waterfall model based on the inclusion of a testing phase for each development stage. It is also known as the verification and validation model. The image below shows a typical V-Model. The traditional shift left approach lowers the testing and thus moves it to the left on the right side of the V-Model. Unit testing and integration testing are the primary focus in the traditional shift left approach. This type of testing is done using API testing and Selenium. However, acceptance testing and system testing are not emphasized heavily. 2. Incremental Approach This shift left strategy best suits projects developing complex and large software systems. Sometimes, it becomes difficult to manage all the tasks and deliverables simultaneously. As a result, they are divided into smaller chunks. These components are built upon one another, and the software is delivered to the customer with each increment. Following each delivery, development, and testing are shifted to the left. This benefits the testing teams to test each component. Thus, it involves incremental testing by having an incremental development cycle. The following image shows an example of this process. 3. Agile/DevOps Approach This shift left testing approach is typically carried out over several sprints. It focuses on continuous testing through an evolutionary life cycle of various smaller sprints. It is primarily used for developmental rather than operational testing, which occurs after the system has been operationalized. 4. Model-Based Approach The main objective of the shift left approach is to catch bugs early. However, in the three models discussed above, testing would begin earlier in the development cycle. As a result, some critical issues are missed during requirements gathering, which is later discovered once the development cycle is completed. Key Strategies to Shift Left Here are some key strategies you can implement to shift your software testing to the left. Planning: It is an essential aspect of the shift left strategy as it serves as a springboard for test life cycle tasks. Testers can better understand future demand by collaborating with management and operational stakeholders. With this insight, you can plan and confirm the budget, resource allocation, and testing strategies. Static testing: It is done in the early stages of a project and comprises requirements and design validation. Using static testing, you can uncover problems early in the project's life cycle before they become too costly to fix. Unified test strategy: With a unified test strategy, you can evaluate constraints on automation, stubs, environments, and test data, guaranteeing that the respective teams can meet the requirements. In general, this is a high-level approach for end-to-end testing, from unit tests to user acceptance tests (UAT) to operational readiness tests (ORT) and post-deployment tests (PDT). This strategy will cover all QA responsibilities and steps. Risk-based analysis: Software risk analysis determines every test case's consequences and probability of failure. Functional, non-functional, and regression testing can all be carried out using this method. How To Implement the Shift Left Strategy? Shift left does more than just help teams find defects early. Now let us find out what your teams need to do to get started with shift left tests. Identify and Plan the Testing Life Cycle Planning is integral to the shift left approach. It works best when the test analysts identify and plan the entire testing life cycle before the start of the actual development process. This provides a strong starting point for all activities in the test life cycle and will help all business and operational stakeholders, developers, and testers understand the project's tasks, objectives, and expected outcomes. One way of doing this is by identifying testing requirements from the project planning and requirements specification phase. The test plan includes budget, resources, testing strategies, and other project requirements. This helps teams focus on quality from day one of the project rather than waiting for defects to be uncovered late in the software development life cycle. Induce a Developer-Based Testing Approach The primary role of a developer is to code new features or enhancements per the requirements. However, testing is no longer an activity done by a tester. As the developers are most familiar with their codes, they can rigorously test their code to rule out any errors and check the application's functionality. It is also essential to ensure that the new code does not give rise to any defects when integrated with the application's existing functionality. So, testing the code as soon as it is developed ensures quicker defect identification. It speeds up exposing and fixing coding errors. It helps in reducing uncertainty in a unit. Development testing aims to eliminate coding errors before the code is passed on to the QA team. A perfect blend of both developer-based and QA-based testing would ensure easy defect identification and quality feature release. Code Review Quality Checks Collaboration is the key to success. To ensure higher code quality, all developers must agree to conform to the same coding standards. With the developers now contributing to the testing efforts, the testers can focus on defining quality checks for the developers' scripts and focus on exploratory, security, and performance testing. Use the Same Tools One of the significant issues faced by the testing team is the inability to create automated tests using the same tools used by the developers. This becomes a roadblock for testers creating the automation framework. A best practice is using the same technology stack the developers use. Feature Testing It is the process of making changes in software to add new features or to modify the already existing features. Testing these features is extremely important, and delivering software incrementally necessitates the development and QA teams to work collaboratively to deliver a build. The actual state of the system in terms of bugs is known after every check-in. With stringent code quality checks, the defects are detected at an early stage and hence are easier to fix, resulting in improved quality of each feature. Engage in Test Automation In this DevOps-driven landscape, it is highly recommended to adopt test automation to maximize the avail benefit of shift left tests. With test automation, developers and testers can automate the entire build-to-test process across all the stages of software development. To reduce testing costs, always use cloud-based testing platforms so your QA teams can access different browsers, devices, and platforms. Challenges of Shift Left Testing As we saw, the shift left strategy offers many benefits. However, everything has its challenges. Here's a limitation of shift left testing. Acceptance The first on the list is acceptance. Shift left testing demands a significant shift in the culture of the organization. Developers and testers accustomed to traditional work processes might find shifting left a deviator. It could disrupt the flow of work, tools, and required skills. How To Overcome It? It is essential to internalize the importance of shift left testing. Moreover, learning sessions advocating the priority help ensure a smooth transition to the new approach. Residual Effort Only some things can be tested early. Shift left testing could involve considerable investment in effort and time if the foundation still needs to be laid. In a situation where we write tests before the development of GUI, there are high chances that GUI characteristics could require more enhancements by the time it is developed fully, so most of your effort has been wasted. How To Overcome It? The development must be done earlier if a specific test needs to be tested early. For example, if API testing is vital for your project and you are trying to do shift left testing, develop the API early. Coverage Have we covered it all? How much automation is needed? Do we have full-proof delivery ready? Answers to these questions can be mind-tickling. How To Overcome It? Defining an intelligent test strategy holds the key to successful automation testing. Here is the list of factors that can help you determine whether you should automate a test case. Automation complexity Average script creation time The desired speed of regression Frequency of releases Stability of the build Rate of change/addition of test cases Best Practices for Shift Left Testing In this section, we discuss some of the best practices one should follow while implementing the shift left approach. Providing Continuous Feedback Continuous feedback allows misalignments and gaps to be fixed rapidly. Also, it gives a better insight to everyone involved and improves them for future projects. To achieve an effective continuous feedback loop, organizations should: Set goals for the meeting. Take detailed notes of the feedback. Ensure an effective communication pipeline. Early Testing Testing early should not imply that testing does not occur in later SDLC stages. Testing early inherently allows early risk mitigation by early defects detection. This does not mean defects cannot emerge in later stages. Therefore, QA specialists and project managers should be prepared and utilize continuous testing. QAs should lay out the degree of value, execution, and operational achievement anticipated from the code so that developers running tests understand what bugs to search for. Automation Testing Each update, release, customization, and integration poses a new threat to the overall quality of the system. In addition, manual testing doesn't meet the demand for faster, higher-quality software development. A viable approach is to use test automation that saves a considerable amount of time for testers. Static Code Analysis Static code analysis is analyzing the code without execution. It goes through the fundamental code structure and guarantees that the code is lined up with various norms and rules. In this analysis, the code is checked against the guidelines and standards. Another perk of static code analysis is that we can automate it. But we should do such an analysis early in the SDLC. Conclusion Getting involved with testing at all points in the continuous development cycle can be daunting. Still, many success stories from the testing community show that it's possible no matter your role. Some experts say the best way to show developers how to test is to be a developer who has tested. Others say a 'stop and think and test/ review' at every stage in our projects. If you have some coding experience or are interested in learning, combining your testing expertise with hands-on development work is a great option. Inarguable that each testing member is cordially responsible for creating high-quality delivery. If your project or organization is trying to shift to the left, the above pointers could be a good handbook for you to be in a position to deliver an excellent product at a faster rate.
Software Testing in Agile Product Development Process (PDP) is an integral part of the development cycle. Therefore, Agile PDP emphasizes the importance of testing throughout the development process to ensure that the software product meets the requirements and expectations of users and stakeholders. Clear and Comprehensive Test Cases To develop effective test cases, review the requirements and use cases, and think through all possible scenarios. These design tests exercise the system under various conditions and ensure that the test cases are well-defined. Therefore, test cases should be well-defined and detailed and include all possible scenarios. The Agile PDP Methodology Includes the Following Key Practices for Software Testing: Test-Driven Development (TDD): In Test-Driven Development, developers write automated tests before writing the actual code. This helps to ensure that the code meets the acceptance criteria defined in the product backlog and that defects are identified and fixed early. Continuous Integration (CI): In Continuous Integration, developers regularly integrate their code changes into a shared repository. This ensures that the software product is continuously tested and defects are identified and fixed early. Automated Testing: Automated testing is an essential practice that helps to reduce the time and effort required for testing. Automated tests can be run quickly and repeatedly, providing instant feedback to the development team; here are some examples of where automated testing can be helpful. Exploratory Testing: In Exploratory Testing, the tester explores the software product to identify any defects or issues that may not be identified through other testing methods. Performance Testing: Automated testing tools have the capability to simulate multiple users, thereby enabling the testing of a system's performance under varying loads. Test Environment The test environment is crucial in Agile Product Development Process to ensure product quality. Steps include identifying requirements, setting up the environment, developing test cases, executing tests, fixing issues, retesting until all test cases pass, and deploying to production. The environment should replicate production, and any issues found during testing should be fixed before deployment. Effective Document Test Results Effective documentation of test results is crucial for assessing product quality in Agile Product Development Process. To do this, define a format, record in detail, categorize by severity, use visual aids, share with the team, and update regularly. This helps identify issues, prioritize them, and provide feedback for improving the product. Involve Stakeholders Agile Product Development Process involves stakeholders to ensure the product meets their needs. Effective involvement includes holding regular meetings, collaborating to define product features, providing feedback opportunities, engaging them in user acceptance testing, and communicating regularly on project progress. This leads to a successful outcome and a better product. Final Decision In Agile Product Development Process, software testing is an integral part of the development process. The final verdict on software testing is that it is never truly finished. Testing is an ongoing process that continues throughout the development cycle, from planning to deployment. Agile teams use various testing techniques to ensure that the product meets the requirements and is of high quality. By involving stakeholders, using continuous integration and delivery, and conducting thorough testing, teams can deliver a successful product that meets the needs and expectations of the end users.
Shift Left and Shift Right are two terms commonly used in the DevOps world to describe approaches for improving software quality and delivery. These approaches are based on the idea of identifying defects and issues as early as possible in the development process. This way, teams can address the issues quickly and efficiently, allowing software to meet user expectations. Shift Left focuses on early testing and defect prevention, while Shift Right emphasizes testing and monitoring in production environments. Here, in this blog, we will discuss the differences between these two approaches: Shift Left and Shift Right. The Shift-Left Approach Shift Left meaning in DevOps, refers to the practice of moving testing and quality assurance activities earlier in the software development lifecycle. This means that testing is performed as early as possible in the development process. Ideally, it is applied at the start, during the requirements-gathering phase. Shift-Left allows teams to identify and fix defects earlier in the process. This reduces the cost and time required for fixing them later in the development cycle. The goal of Shift Left is to ensure that software is delivered with higher quality and at a faster pace. Shifting left meaning in DevOps involves different aspects. Here are the key aspects of the Shift-Left Approach in DevOps: Early Involvement: The Shift-Left Approach involves testing and quality assurance teams early in the development process. This means that testers and developers work together from the beginning rather than waiting until the end. Automated Testing: Automation plays a key role in the Shift-Left Approach. Test automation tools are used to automate the testing process and ensure that defects are detected early. Collaboration: Collaboration is key to the Shift-Left Approach. Developers and testers work together to ensure that quality is built into the product from the beginning. Continuous Feedback: The Shift-Left Approach emphasizes continuous feedback throughout the development process. This means that defects are identified and fixed as soon as they are discovered, rather than waiting until the end of the SDLC. Continuous Improvement: The Shift-Left Approach is focused on continuous improvement. By identifying defects early, the development team can improve the quality of the software and reduce the risk of defects later in the SDLC. After knowing the shift left meaning, let’s see some examples too. Here are some examples of Shift Left practices in DevOps: Test-Driven Development (TDD): Writing automated tests before writing code to identify defects early in the development process. Code Reviews: Conducting peer reviews of code changes to identify and address defects and improve code quality. Continuous Integration (CI): Automating the build and testing of code changes to catch bugs early and ensure that the software is always in a deployable state. Static Code Analysis: Using automated tools to analyze code for potential defects, vulnerabilities, and performance issues. The Shift Right Approach Shift Right in DevOps, on the other hand, refers to the practice of monitoring and testing software in production environments. This approach involves using feedback from production to improve the software development process. By monitoring the behavior of the software in production, teams can identify and resolve issues quickly. This allows users to gain insights into how the software is used by end users. The goal of Shift Right is to ensure that software is reliable, scalable, and provides a good user experience. This approach involves: Monitoring production systems, Collecting feedback from users, and Using that feedback to identify areas for improvement. Here are the key aspects of the Shift Right Approach in DevOps: Continuous Monitoring: Continuous monitoring of the production environment helps to identify issues in real time. This includes monitoring system performance, resource utilization, and user behavior. Real-World Feedback: Real-world feedback from users is critical to identifying issues that may not have been detected during development and testing. This feedback can be collected through user surveys, social media, and other channels. Root Cause Analysis: When issues are identified, root cause analysis is performed to determine the underlying cause. This involves analyzing logs, system metrics, and other data to understand what went wrong. Continuous Improvement: Once the root cause has been identified, the DevOps team can work to improve the system. This may involve deploying patches or updates, modifying configurations, or making other changes to the system. Here are some examples of the Shift Right Approach: Monitoring and Alerting: Setting up monitoring tools to collect data on the performance and behavior of the software in production environments. Also, setting up alerts to notify the team when issues arise. A/B Testing: Deploying multiple versions of the software and testing them with a subset of users. This helps teams to determine which version performs better in terms of user engagement or other metrics. Production Testing: Testing the software in production environments to identify defects that may only occur in real-world conditions. Chaos Engineering: Introducing controlled failures or disruptions to the production environment to test the resilience of the software. Both Shifts Left, and Shift Right approaches are important in DevOps. They are often used together to create a continuous feedback loop that allows teams to improve software delivery. The key is to find the right balance between the two. This can easily be done using the right DevOps platform and analyzing business needs. Understanding the Differences Between Shift Left and Shift Right Shift Left and Shift Right are two different approaches in DevOps that focus on different stages of the software development and deployment lifecycle. Here are some of the key differences between these two approaches: Focus Shift Left focuses on testing and quality assurance activities that are performed early in the software development lifecycle. While Shift Right focuses on monitoring and testing activities that occur in production environments. Goals The goal of Shift Left is to identify and fix defects early in the development process. This helps to ensure that software is delivered with higher quality and at a faster pace. The goal of Shift Right is to ensure that software is secure, reliable, scalable, and provides a good user experience. Activities Shift Left activities include unit testing, integration testing, and functional testing, as well as automated testing and continuous integration. Shift Right activities include monitoring, logging, incident response, and user feedback analysis. Timing Shift Left activities typically occur before the software is deployed, while Shift Right activities occur after deployment. Risks The risks associated with Shift Left are related to the possibility of missing defects that may only be discovered in production environments. The risks associated with Shift Right are related to the possibility of introducing changes that may cause production incidents or disrupt the user experience. Conclusion Both Shifts Left, and Shift Right approaches are critical for the success of microservices. Hope, after reading this article, you’ve got a clear idea of Shifting left meaning and Shifting Right meaning. By using Shift Left and Shift Right, developers can ensure that their microservices are reliable, scalable, and efficient. In addition, these approaches help to ensure that microservices are adopted with security and compliance.
If you ask people to come up with popular attributes for “Agile” or “agility,” Scrum and Jira will likely be among the top ten featured. Moreover, in any discussion about the topic, someone will mention that using Scrum running on top of Jira does not make an organization Agile. However, more importantly, this notion is often only a tiny step from identifying Jira as a potential impediment to outright vilifying it. So, in March 2023, I embarked on a non-representative research exercise to learn how organizations misuse Jira from a team perspective as I wanted to understand Jira anti-patterns. Read on and learn more about how a project management tool that is reasonably usable when you use it out of the box without any modifications turns into a bureaucratic nightmare, what the reasons for this might be, and what we can do about it. The Organizational Rationale Behind Regulating Jira Organizations might use Jira in restrictive ways for various reasons, although these reasons rarely align with the agile mindset. Some reasons include the following: Control and Oversight: Management might want to maintain control and supervision over a Scrum team’s work, ensuring that the team follows established processes and guidelines. A desire for predictability and standardization across the organization can drive this. Risk Aversion: Organizations may be risk-averse and believe tighter controls will help minimize risks and prevent project failures. This approach might stem from previous negative experiences or a need to understand agile principles better. Compliance and Governance: In some industries, organizations must adhere to strict regulatory and governance requirements. This requirement can lead to a more controlled environment, with less flexibility to adopt agile practices fully. Hierarchical Culture: Organizations with a traditional, hierarchical structure may have a top-down approach to decision-making. This culture can make it challenging to embrace agile principles, which emphasize team autonomy and self-organization. Inadequate Understanding of Agile Principles Such as Scrum: Some organizations may not fully understand them or misconstrue them as lacking discipline or structure. This misunderstanding can result in excessive control to compensate for the perceived lack of process. Metrics-Driven management: Management might focus on measurable outputs, such as story points or velocity, to assess a Scrum team’s performance. This emphasis on metrics can lead to prioritizing numbers over the actual value delivered to customers. Resistance to Change: Organizations that have successfully used traditional project management methods may resist adopting agile practices. This resistance can manifest as imposing strict controls to maintain the status quo. After all, one purpose of any organization is to exercise resilience in the face of change. While these reasons might explain why organizations use Jira in restrictive ways, curtailing the agile mindset and a Scrum team’s autonomy or self-management will have negative consequences. For example, restrictive practices can: Reduce a team’s ability to adapt to change, Hinder collaboration, Decrease morale, and Diminish customer value created. Contrary to this, agile practices promote flexibility, autonomy, and continuous improvement, which organizations will undermine when imposing excessive control, for example, by mandating the use of Jira in a particular way. Jira Anti-Patterns Gathering Qualitative Data on Jira Anti-Patterns I did not run a representative survey to gather qualitative data for this article. Instead, I addressed the issue in a LinkedIn post on March 16, 2023, that received almost 100 comments. Also, I ran a short, non-representative survey on Google Forms for about two weeks, which resulted in 21 contributions, using the following prompt: “Jira has always been a divisive issue, particularly if you have to use Jira due to company policy. In my experience, Jira out-of-the-box without any modification or customization is a proper tool. If everyone can do anything, Jira is okay despite its origin as a ticket accounting app. The problems appear once you start submitting Jira to customization. When roles are assigned and become subject to permissions. Then, everything starts going south. I want to aggregate these Jira anti-patterns and make them available to provide teams with a data-backed starting point for a fruitful discussion. Then, they could improve their use of the ticketing tool. Or abandon it for a better choice?” Finally, I aggregated the answers to identify the most prevailing Jira anti-patterns among those who participated in the LinkedIn thread or the survey. Categories of Jira Anti-Patterns When I aggregated the effects of a mandated rigid Jira regime, they fall into four main categories: Loss of autonomy: Imposing strict controls on the Jira process can reduce a team’s autonomy and hinder their ability to self-manage, a fundamental principle of agile development. Reduced adaptability: Strict controls may prevent the team from adapting their processes based on feedback or changing requirements, resulting in diminished value creation. Bureaucracy: Increased oversight and control can introduce unnecessary bureaucracy, slowing the team’s work by creating unnecessary work or queues. Misalignment with agile principles: Imposing external controls can create misalignment between the organization’s goals and agile principles, potentially hindering the teams from reaching their true potential and undermining the return on investment of an agile transformation. Jira Anti-Patterns in Practice The most critical Jira anti-patterns mentioned by the participants are as follows: Overemphasis on Hierarchy: Using Jira to enforce a hierarchical structure, thus stifling collaboration, self-management, and innovation. For example, roles and permissions prevent some team members from moving tickets. Consequently, teams start serving the tool; the tool no longer supports the teams. Rigid Workflows: Creating inflexible and over-complicated workflows that limit a Scrum team’s ability to inspect and adapt. For example, every team has to adhere to the same global standard workflow, whether it fits or not. Administration Permissions: Stripping teams of admin rights and outsourcing all Jira configuration changes to a nearshore contractor. Micromanagement: Excessive oversight that prevents team members from self-managing. For example, by adding dates and time stamps to everything for reporting purposes. Over-Customization: Customizing Jira to the point where it becomes confusing and difficult to use; for example, using unclear issue types or useless dashboards. Over-Reliance on Tools: Relying on Jira to manage all aspects of the project and enforcing communication through Jira, thus neglecting the importance of face-to-face communication. Siloed Teams: Using Jira to create barriers between teams, hindering collaboration and communication. Turning Teams Into Groups of Individuals: Dividing Product Backlog items into individual tasks and sub-tasks defies the idea of teamwork, mainly because multiple team members cannot own tasks collectively. Lack of Visibility I: Hiding project information or limiting access to essential details, reducing transparency. Lack of Visibility II: Fostering intransparent communication, resulting from a need to bypass Jira to work effectively. Fostering Scope Creep: Allowing the project scope to grow unchecked as Jira is excellent at administering tasks of all kinds. Prioritizing Velocity over Quality: Emphasizing speed of delivery over the quality of the work produced. For example, there is no elegant way to integrate a team’s Definition of Done. Focus on Metrics Over Value: Emphasizing progress tracking and reporting instead of delivering customer value. For example: Using prefabricated Jira reports instead of identifying the usable metrics at the team level. Inflexible Estimation: Forcing team members to provide overly precise task time estimates while lacking capabilities for probabilistic forecasting. Some Memorable Quotes from Participants There were some memorable quotes from the participants of the survey; all participants agree to a publication: Jira is a great mirror of the whole organization itself. It is a great tool (like many others) when given to teams, and it is a nightmare full of obstacles if given to old-fashioned management as an additional means of controlling and putting pressure on the team. The biggest but most generalized one is the attempt to standardize Jira across an org and force teams to adhere to processes that make management’s life easier (but the teams’ life more difficult). It usually results in the team serving Jira rather than Jira serving the team and prevents the team from finding a way of working or using the tool to serve their individual needs. This manifests in several ways: forcing teams to use Company Managed Projects (over team Managed ones), mandating specific transitions or workflows, requiring fields across the org, etc. Stripping project admins of rights, forcing every change to a field to be done by someone at a different timezone. The biggest anti-patterns I have seen in Jira involve over-complicating things for the sake of having workflows currently match how organizations currently (dys)function vs. organizations challenging themselves to simplify their processes. The other biggest anti-pattern is using Jira as a “communication” device. People add notes, tag each other, etc., instead of having actual conversations with one another. Entering notes on a ticket to create a log of what work was completed, decisions made, etc., is incredibly appropriate but the documentation of these items should be used to memorialize information from conversations. I can trace so many problems back to people saying things like, “Everyone should know what to do; I put a note on the Jira ticket.” Breaking stories up into individual tasks and sub-tasks destroys the idea of the team moving the ball down the court to the basket together. Developer: “Hey, I’ve wanted to ask you some questions about the PBI I’m working on.” Stakeholder: “I’ve already written everything in the task in Jira.” Another anti-pattern is people avoiding Jira and coming directly to the team with requests, which makes the request “covert” or “Black Ops” work. Jira is seen as “overhead” or “paperwork.” If you think “paperwork” is a waste of time, just skip the “paperwork” the next time you go to the bathroom! Implementing the tool without any Data Management policies in place, turning into hundreds of fields of all types (drop-down, free text, etc.). As an example, there are 40 different priority options alone. Make sure to have a Business Analyst create some data policies BEFORE implementing Jira. “A million fields”: having hundreds of custom fields in tickets, sometimes with similar names, some with required values. I have seen tickets of type “Task” with more than 300 custom fields. “Complex board filters with business rules”: backlog items are removed from boards based on weird logic, for example a checkbox “selected for refinement.” How to Overcome Jira Anti-Patterns When looking at the long list of Jira anti-patterns, the first thought that comes to mind is: What can we do to counter these Jira anti-patterns? Principally, there are two categories of measures: Measures at the organizational level that require the Scrum teams to join a common cause and work with middle managers and the leadership level. Measures at the Scrum team level that the team members can take autonomously without asking for permission or a budget. Here are some suggestions on what to do about Jira anti-pattern in your organization: Countermeasures at the Organizational Level The following Jira anti-patterns countermeasures at the organizational level require Scrum teams to join a common cause and work with middle managers and the leadership level: Establish a Community of Practice and Promote Cross-Team Collaboration: Create a cross-functional community of practice (CoP) to share knowledge, experiences, and best practices related to Jira and agile practices. Revisit Governance Policies: Work with management to review and adapt governance policies to support agile practices such as Scrum better and reduce unnecessary bureaucracy. Train and Educate: Support the middle managers and other stakeholders by providing training and educational resources to increase their understanding and adoption of agile principles. Encourage Management Buy-In: Advocate for the benefits of “Agile” and demonstrate its value to secure management buy-in and reduce resistance to change. Share Success Stories: Promote successes and improvements from agile practices and how Jira helped achieve them to inspire and motivate other teams and departments. Foster a Culture of Trust: Work with leadership to promote a culture of trust, empowering Scrum teams to make decisions and self-manage. Review Metrics and KPIs: Collaborate with management to review and adjust the metrics and KPIs used to evaluate team performance, prioritizing outcome-oriented customer value over output-based measures. Customize Jira Thoughtfully: Engage with management and other Scrum teams to develop a shared understanding of how to customize Jira to support agile practices without causing confusion or adding complexity while delivering value to customers and contributing to the organization’s sustainability. Address Risk Aversion: Work with leadership to develop a more balanced approach to risk management, embracing the agile mindset of learning and adapting through experimentation. Countermeasures at the Organizational Level Even if a Scrum team cannot customize Jira independently due to an organizational policy, there are some measures the team can embrace to minimize the impact of this impediment: Improve Communication: Encourage open communication within the team and use face-to-face or video calls when possible to discuss work, reducing the reliance on Jira for all communications. Adapt to Constraints: Find creative ways to work within the limitations of the Jira setup, such as using labels or comments to convey additional information or priorities, and share these techniques within the team. Limit Work-In-Progress: Encourage team members to work on a limited number of tasks to balance workload and avoid task hoarding, even if the team cannot enforce WIP limits within Jira. Emphasize collaboration: Encourage a collaborative mindset within the team, promoting shared ownership of tasks and issues, although Jira does not technically support co-ownership. Adopt a Team Agreement: Develop an agreement for using Jira effectively and consistently within the team. This Jira working agreement can help establish a shared understanding of best practices and expectations. Conclusion To use a metaphor, Jira reminds me of concrete: it depends on what you make out of it. Jira is reasonably usable when you use it out of the box without any modifications: no processes are customized, no rights and roles are established, and everyone can apply changes. On the other hand, there might be good reasons for streamlining the application of Jira throughout an organization. However, I wonder if mandating a strict regime is the best option to accomplish this. Very often, this approach leads to the Jira anti-patterns mentioned above. So, when discussing how to use Jira organization-wide, why not consider an approach similar to the Definition of Done? Define the minimum of standard Jira practices, get buy-in from the agile community to help promote this smallest common denominator, and leave the rest to the teams. How are you using Jira in your organization? Please share your experience with us in the comments.
Agile and Scrum are two related concepts that are often used in software development. Agile is an umbrella term encompassing a set of values and principles for software development, while Scrum is a specific framework within the Agile methodology. Agile emphasizes collaboration, flexibility, and adaptability, as well as the ability to respond to change. It emphasizes, In addition, iterative development and continuous improvement, where teams work in short cycles called sprints, with frequent feedback and re-evaluation. Scrum is a framework for implementing Agile methodology that provides a structure for managing and completing projects. It emphasizes teamwork, accountability, and iterative progress. Scrum involves a set of roles, ceremonies, and artifacts that help teams work together effectively, such as sprint planning, daily stand-ups, sprint reviews, and retrospectives. What Is Agile Methodology? Agile methodology is an exercise that helps continuous iteration of development and testing in the Software development life cycle process. Basically, Agile breaks the product into a few smaller builds. It also encourages teamwork and face-to-face communication. In agile methodology, Businesses, stakeholders, developers, and clients must work together to develop high-quality products. An agile methodology is an approach to project management that emphasizes flexibility, collaboration, and customer satisfaction. It is a set of values and principles prioritizing responding to change over following a rigid plan. What Is Scrum Methodology? Scrum is an Agile software development methodology that is commonly used in testing. It is an iterative and incremental approach to software development that focuses on delivering working software in short time frames, known as sprints. In Scrum, the testing process is integrated into the development process, with testers working collaboratively with developers, product owners, and other team members. The testing process is continuous throughout the development lifecycle, with tests being automated where possible and executed at every stage of development. Overall, the Scrum methodology emphasizes collaboration, communication, and continuous improvement, making it an effective approach to testing in software development. Difference Between Agile and Scrum Agile Scrum Agile development is based on iterative and incremental approaches. Scrum is the implementation of the agile process. In this process, after every two to three weeks, we deliver the incremental build to the customer. Agile software development has been widely used in small but expert project development teams. Scrum is generally used in that projects where the requirement is changing frequently. Compared to Scrum, agile methodology is a more rigid method, so we cannot change the requirement frequently. The important advantage of Scrum to use nowadays is its flexibility, as it quickly reacts to changes. The agile methodology involves participation and face-to-face interactions between the team members of various cross-functional teams. In Scrum, participation is cognizable in a daily stand-up meeting with a fixed role assigned to each team member. In Agile Methodology, we will Deliver and update the software daily. In Scrum, When the team is done with the current sprint activities, after that, we can plan the next sprint. In Agile methodology, there is a Project head who takes care of all the tasks related to the project. In Scrum, there is no team leader, so the entire team addresses the issues or problems related to the project. In Agile Design and execution should be kept simple and easy to understand. In Scrum methodology, design, and execution is innovative and experimental. Conclusion Agile and Scrum are two related concepts that have become popular in software development and project management. Agile is a philosophy that emphasizes flexibility, collaboration, and iterative development, while Scrum is a specific framework that puts these principles into practice. The Agile approach to software development and project management is designed to be responsive to change, with a focus on delivering value to the customer in short, iterative cycles. Scrum is a framework within the Agile methodology that provides a structured approach to managing and organizing teams to deliver projects in an Agile way. One of the key benefits of Agile and Scrum is that they allow teams to be more responsive to changes in project requirements and customer needs, as well as enable faster delivery of working software. This approach also encourages collaboration and communication among team members, leading to better outcomes and higher-quality work. Overall, Agile and Scrum have proven to be effective methodologies for managing complex projects, particularly in software development. However, as with any approach, their success depends on proper implementation and adaptation to the specific needs of each project and team.
All We Can Aim for Is Confidence Releasing features is all about confidence: confidence that features work as expected; confidence that our work is based on quality code; confidence that our code is easily maintainable and extendable; and confidence that our releases will make happy customers. Development teams develop and test their features to the best of their abilities so that quality releases occur within a timeframe. The confidence matrix shown below depicts four main areas: The high confidence and small release time area (an area that all development teams strive for) The low confidence and small release time area The high confidence and long release time area The low confidence and long release time area The first is when we’ve made a quality release quickly. The second is when we quickly released features that may be buggy. The third is when it took us a while to do a quality release. The fourth is when it took us a while to make a buggy release. Think of the confidence matrix as a return on investment (ROI) matrix in its most basic form where our return is confidence and our investment is time. When feature development starts, confidence could be high or low. We may be confident that we know what we must develop and how to do it. I’ve found that most software projects start in the low-confidence zone. New features could mean new unknowns that result in low confidence. Most importantly, as our development and testing activities continue, as our release time reaches the deadline, our confidence should increase. Unfortunately, this is not always the case. To achieve confidence, most teams test and use development best practices. Despite their best efforts, I’ve seen teams releasing fast or slow with high or low confidence. Teams’ confidence may have started low but finished high or vice versa. This article shares experiences about how teams have tried to gain confidence from testing. Confidence From Tests Requires Reliable Tests Tests will either pass or fail. We execute them to get a true picture of the system under test. The system could be a unit or units of code or a complete application. The true picture could be that a new feature is ready to be released or that there are problems that need to be fixed before releasing. Once we’ve got the true picture we can make decisions based on testing results and not guesses. How do we know that we’ve got the true picture? By trusting our testing results. Trusting our testing results means that no matter how many times we execute a test suite, all the tests will have no false positives and no false negatives. Tests should not pass accidentally. For example, if out of ten runs they pass five times and fail five times, they are not reliable. Such testing results are as good as guesses and will not give us a true picture of the system under test. A test may be failing for irrelevant reasons while the functionality that it exercises could be working as expected. We need to have reliable tests where we can trust our test results. No matter how much code we cover with tests, no matter how fast or slow our tests run, we will get confidence from our testing efforts if and only if our tests are reliable. Levels of Testing: Speed vs Scope A simple way to understand scope is the following rule of thumb: large scope means that we cover many lines of code. Small scope means that we cover a few lines of code. Traditionally, there are four testing levels. The lower level is unit testing, followed by integration testing, system testing, and acceptance testing, which is the higher testing level.Unit testing is about making educated decisions about what inputs should be used and what outputs are expected per input. Groups of inputs should be identified that have common characteristics and are expected to be processed in the same way by the unit of code under test. This is known as partitioning, and once such groups are identified, they should be covered by unit tests. Unit tests have a small scope. To cover our code thoroughly we need many unit tests. This is usually not a problem because we can run thousands of them in a few seconds. As we go from lower to higher testing levels the scope increases and test execution speed becomes an issue. Once a unit of code is defined we may also define components of code by grouping code units together. Integration testing is about interactions and interfacing between different components. Compared with unit tests, integration tests have a larger scope, but are roughly at the same order of magnitude when it comes to test execution speed. At a system level, our product is tested at a large scope. A single system test could cover thousands of units and hundreds of components of code. Such tests take time to execute. If we could build confidence without needing thousands of them, then that would be good news. The bad news is that test execution speed is so low that it could prolong our feature releases considerably. Similar to system tests, acceptance testing has a large scope. In some companies, it is performed by customers or company team members at the customer’s site. Other companies use acceptance testing as validation testing performed by the customers. Speed Is Vital To release a feature, we could test to gain confidence that it works as expected, functionally and non-functionally. It takes time to build confidence. We need time to perform development and testing, assess our testing results, and make a decision about releasing or not. Are we good to release or should we fix the bugs we’ve found, redeploy to test that all fixes are OK, and then release? To minimize the feature-release time, we need to minimize at least: The time it takes to develop the feature: Using coding best practices during development is one way to introduce fewer bugs. The time it takes to test: We test to find bugs. Are they important? We should fix and redeploy. Are they not important? We could deploy with known issues. There are teams that fix a bug, deploy the bug fix in a testing environment, test that the bug fix works as expected and that it does not introduce any new issues, and then deploy to production. Others deploy bug fixes directly to production (this is faster but could be riskier). Release speed is vital. Depending on how much time we’ve got to release a feature, I’ve seen teams making various decisions in order to handle deadlines. These included: Features are released without testing while coding standards used for development are questionable. An example of this is a team that usually started and finished their development efforts in the low confidence area. The team has had a hard time understanding why a number of problems have arisen after their releases. Most importantly, the most critical problems remained under their radar for a long time. Features are released without testing while other coding standards are met and developers are confident with their code. There was a team of experienced developers that did not believe in testing. The closer to testing that they would get would be debugging their code. They were usually between the high confidence/small release time and high confidence/long release time areas in the confidence matrix. Bugs could have fallen under their radar occasionally and testers from other teams would be brought for QA testing when the team was about to release features with rich functionality. Features are released with just a few unit or integration-level tests but a large number of UI tests. This is a case that I’ve seen many times. Such teams would fall in any of the four areas in the confidence matrix. When showstopper bugs were found late by the testers and when fixing them required major rewrites from developers, the team's confidence was low and the release deadlines may have been prolonged. Even if no showstoppers were found, testing was a bottleneck. Developers were reluctant to change the code in a number of areas and each change called for extensive regression testing at the UI from QA testers. When releasing features with rich functionality, QA testing at the UI was a bottleneck because the test execution speed was low and the tests were many. UI test automation has helped to overcome this problem for some teams while for other teams it gave a smaller ROI than expected. Features are released with a large number of unit and integration tests and a minimal set of UI tests. Such teams would usually fall in the high confidence/small release time area of the confidence matrix. Bugs may occasionally have gone under the radar, especially for features with rich functionality but they were usually fixed quickly without side effects. They had continuous integration and continuous deployment setup. Their continuous builds were made of unit tests and integration tests. Frequently executing unit and integration tests was the main source of their confidence. A final confidence boost was given by a small number of manual exploratory tests in the UI. Features released with a large number of integration tests, a number of unit tests, and a few UI tests. This was the case for teams that used microservices and teams that executed a large number of front-end tests. Some JavaScript frontend developers, for example, were strong believers of the “write tests, not too many, mostly integration” paradigm. In the case of backend developers writing microservices, they believed that in a world of microservices, the biggest complexity is not within the microservice itself, but in how it interacts with others. As a result, they gave special attention to writing tests exercising interactions between microservices. Such teams usually avoided the low confidence and long release time area in the confidence matrix. Following good coding standards and best coding practices does not mean that we should not test. In fact, testing is another best practice for coding. As this article focuses on testing and not other coding standards, it suffices to mention that testing is always a good idea. However, when developing and testing, our release speed will be affected by our testing speed, too. Testing dynamics per testing level need to be taken into account, in order to get the most value from our testing efforts for the allocated time. Test Execution Speed Testing at any level is important and necessary. The lower the testing level the faster the test execution speed. I’ve witnessed at least three ways that test execution speed has affected how development teams work. To identify what compromises to make: If we must make compromises, make an educated decision about what to do and what to avoid. Depending on how much time we have for testing, I've seen teams choosing at what testing level they should test. Ideally, if time and costs were not a constraint, we should test at all levels possible. This is because 100% test coverage at a unit level does not mean that we will catch no bugs with integration testing and/or with system testing. The same is true for each testing level. However, a test suite of 1000 unit tests may take an hour to complete while a UI automation suite with 200 tests may take one day to complete. Although choosing not to test at any level may involve risks, if we have little time to dedicate to testing we may make educated decisions according to what tests we want to run and at what level. To identify how fast we will get feedback from our tests: The test result is our feedback. Did the test pass? Our feedback is a green light. Did the test fail? Our feedback is a red light. A development team tested first the most important functional and non-functional areas of their release. They first tested at a testing level that test execution speed was the fastest. As a result, showstopper bugs could be found early during testing and hence they were also fixed early without jeopardizing release time. The main factor that lowered their confidence was showstoppers that were found late and fixed late, resulting in missing release deadlines. They’ve found that the best way to allocate their testing efforts was to start with quick feedback testing (unit and integration testing). If no showstoppers are found, then for the remaining time, continue with higher-level testing. To help identify our testing levels: People often go back and forth about whether particular tests are unit tests or integration tests. Large unit tests could also be considered small integration tests and vice-versa. But what are they really and at what level do they belong? There was a team that shared a definition like, "If a test talks to the database or if it communicates across the network, if it involves accessing file systems like editing configuration files, then it’s not a unit test." The reasoning behind this was simple: test execution speed. If a test talked to the database, for example, then it would take longer to execute. Since unit tests are the fastest across all testing levels, the team decided to call low-level tests that performed such time-consuming actions as integration tests. Another team was using fault detection time as a guide. Α test failed. If it took seconds to detect the fault in the code that caused the failure, then the failing test was a unit test. If it took minutes to detect the fault then the failing test was an integration test. There was a group that used architects and tech leads to write a few integration tests. Their main goal was to ensure that the choreography and orchestration of the architectural components were working. Such tests usually covered 10 to 20% of the code at maximum and having a large scope they usually were slow. In another group, QA and business analysts wrote acceptance tests to achieve a maximum of 50% of code coverage. They also wrote a few system tests as final tests of choreography and orchestration. The system tests covered very little of the actual business rules and were the slowest. Wrapping Up There is a popular debate about what percentage of tests to write at what testing level. I’ve tried to shift the focus a little bit on confidence over time. It’s all about confidence, and a great deal of it can be achieved by running tests quickly and reliably. Testing closer to the unit/integration level will be quicker and necessary, but not sufficient. Higher testing levels will also need to be covered which will probably cost more in execution time, maintenance, and reliability. Let’s not forget one of our basic prerequisites for tests to be valuable: tests that pass for the right reasons and fail for useful reasons. I’ve shared a number of experiences about how different development teams managed their testing efforts resulting in different levels of confidence over time.
Stefan Wolpers
Agile Coach,
Berlin Product People GmbH
Søren Pedersen
Co-founder,
BuildingBetterSoftware
Hiren Dhaduk
CTO,
Simform
Daniel Stori
Software Development Manager,
AWS