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The dream of any software engineering team and organization is to achieve success in their software and impact the business directly. However, the most challenging point is explaining how to achieve this success and get metrics. In this video, you'll learn more about this through strategy and tactics in software engineering. When we talk about the metrics, it becomes even more challenging mainly because we look at the metrics focused on tech, such as the number of tasks, commits, more code, and deployment frequency. But delivering a version does not necessarily mean you're impacting the business. Let's start talking about this mistake: the passion for tech. Technology as an End: The Most Significant and Current Mistake As software engineers, we love what we do and have a huge passion, bringing us to the most trivial mistake: we became tech fanboys. Thus, we start with a tool instead of the focus on the solution. We can see it in Forbes's 16 Obstacles To A Successful Software Project, where the authors listed several topics. I'll put on highlight a few: Hyper-focused planning and design Unclear or undefined client expectations Unexpected complexities Poor collaboration between the product and engineering teams “It is common for developers to deliver a product that doesn’t align with the client’s perceptions.” When we check those points, it looks like a paradox, because we spend a lot of time planning while we don't deliver what the client wants, and poor collaboration with the product team. Fatally, we are offering complexity. Thus, what are you providing instead if you're spending time on what is not software? The unexpected complexity became a considerable enemy that is killing software productivity. We usually want to solve the problem and believe the focus is the new tool instead of understanding the context. Every time it happens, I ask myself, "Did we forget what a software engineer is?" What Is Software Engineering? Talking about this definition, let's first understand the etymology: Engineering is from the Latin ingenium, meaning "cleverness," and ingeniare, meaning "to contrive, devise." In building something to achieve a goal, we can use classic engineering with an aqueduct where reaching is not masonry but moving water to the city. Keeping with Roman history, we also can use military engineering with Caesar's Rhine Bridge, where Julius Cesar decided to build a bridge to cross an army of forty thousand soldiers in ten days. It succeeded because it had strategy and tactics, where the strategy handled the goal and tactics care of getting things done. The goal was not to build a bridge but to cross the troops; its building was the way to archive it. In software engineering, my favorite definition comes from Modern Software Engineering by David Farley, where: "Software engineering is applying an empirical, scientific approach to finding efficient, economical solutions to practical problems in software." Getting the goal is the first step, and an efficient, economical solution is what we want as software engineers. Julius Cesar wished to move the troops across the river. If the boats got cheaper, it would be the solution. As a younger engineer, he uses the approach of strategy and tactics, where we can see the software architecture as the strategy and design as tactics. Indeed, we have several definitions of software architecture, and we don't want to spend time standardizing the term: unifying all those terms has a link with the goal. We can use it in the Fundamentals of Software Architecture with the second law of architecture: Why is more important than how, where we can remember the start with the why by Simon Sinek. Strategy and Tactics Combining strategy and tactics based on the context is crucial to achieving success where the root is the context. There is no silver bullet: we need to understand the context and then apply what it needs. We'll list this combination on six points: each item has a tactic and strategic perspective. Software Engineering Design Architecture Code Design Get done Code is not enough for software DDD Entity, repository Bounded context, ubiquitous language Documentation On Code, Changelog, README, Git commit message C4-model, Tech-radar, ADR, communication Cloud The Twelve-Factor App Delegate what you don't want to focus Test Unit test, coverage test, mutation testing Pyramid Test Video We'll listen briefly and provide more details in the video below:
This is part two of my series on the OCP-17. In my first post, I explained why it’s still worthwhile to tackle the trick questions and arcane details of this exam. Each of us has our own sense of how much knowledge is good enough to get a job done, like writing Java code. The examiners at Oracle probably put the bar much higher than you. If you can’t somehow make the journey fun, you’re bound to give up. So, your motivation needs an upgrade, and you must put in the right practice. In this post, we will look at both. Amateur Versus Professional Practice Not all practice makes perfect. Poor practice doesn’t even make you proficient. It’s not the hours that count, although Malcolm Gladwell in Outliers claimed you need at least 10,000. This sweeping statement with its conveniently round number is questionable science, to say the least. Excellence itself is not a well-defined notion. People’s innate aptitudes are not created equal. Some are born geniuses. But most importantly, the hours you put in must be effective. Amateur practice is fun, but sub-optimal. When amateur musicians pick up their instrument to learn a favorite tune, they play that tune from beginning to end, again and again until it sounds sort of okay. Professionals and serious amateurs don’t do that. They zoom in on the hard bits and massage these pain points until they stop hurting – in classical music, ten percent of the notes require ninety percent of your attention. In addition, they practice chords and scales. They record themselves and listen back. The goal of practice is focused improvement through continuous feedback. It’s hard work, aimed to ensure a performance that feels like second nature. Cry in the dojo, laugh on the battlefield, as the martial arts saying goes. The Okay Point of Memory Athletes The above touches on the "okay point," a concept I learned from Joshua Foer’s book Moonwalking with Einstein. In this enjoyable piece of participatory journalism, Foer went deep into the wonderful world of competitive memory and aced the American championships a year later. All the memory athletes he met (nearly all men – why’s that?) explained that their impressive feats came down to tricks already known to the ancient Greeks. The brain is terrible at remembering arbitrary data, like numbers or playing cards. It evolved to be great at pattern recognition and spatial mapping, so our hunter-gatherer ancestors could remember which mushrooms were tasty, under which tree they stashed them, and how to tell them apart from the similar-looking death cap. Your brain can store vastly more random stuff than just your parents’ landline number. With the right technique, you too can memorize a shuffled deck of cards. But it takes a special personality to muster the motivation to do that, let alone pi to a thousand decimals. The acquisition of new knowledge stalls when you feel the job is done. Mental athletes set the bar crazy high and don’t care about utility. Me, I’m satisfied with a mediocre 3.14159. Mediocrity has a disapproving ring to it, but we accept to be average at most things in life. That includes the niches in our chosen field of IT. There’s not enough time to be an all-around expert. But if you make a living as a software developer, don’t relegate the fundamentals of writing code to AI tooling. We’ve all been doing it, present company included. The friendly omniscient butler will turn out a wolf in sheep’s clothing as he lowers your human okay point. Let’s raise the bar uncomfortably high and find a way to turn the jar of dry oatmeal that is the exam requirements into a gourmet meal. In other words, let’s work on our motivation. Neither Carrots Nor Sticks Motivation can be extrinsic, intrinsic, or a mix of both. So|| rather than ^. Extrinsic motivations imply rewards and penalties, hopefully not too many of the latter. Many exams are a must-have. You’ll have heard of the infamous Knowledge exam that London cabbies must still take, Waze be damned. Security professionals cannot work for the military without up-to-date certifications. If you’re a full-time Java programmer, the OCP is a wise and worthwhile investment from a career development perspective, even if your employer doesn’t demand it. And if they do, that’s your motivation, right there. But the promise of a promotion or the specter of dire consequences doesn’t make preparation any more enjoyable. It only adds to the pressure. It doesn’t raise your okay point because that indicates the level of your intrinsic motivation. When I play the piano, there are no external carrots or sticks. No audience or record producer is waiting for my rendition of Mozart’s piano sonata in A minor. I skip the presto movement entirely because it’s just too difficult. Only intrinsic motivation that makes the practice itself appealing can raise my okay point and motivate me to work harder. If you love the effort itself more than the fruits of the effort, you have tapped into your intrinsic motivation. Take the memory champs again. There is no career benefit to be had from their pains. There’s no utility in memorizing your shopping list. It doesn’t even make you very popular, except in a very small elite. They loved the effort for its own sake. Their motivation was entirely intrinsic. My motivation to write for DZone is similar. Sure, I get some professional kudos and recognition, which is nice. But to be honest, that’s never sufficient motivation for my next article. I simply love the act of writing and the satisfaction of a job well done. Professional growth is an important part of your motivation for the OCP, but that’s still extrinsic. You must add a touch of playfulness to make it stick. The rabbit keeps chewing until bellyful() is true or a NoMoreCarrotsException is thrown. That brings a smile to my face. Fun Is the Glue That Makes Everything Stick When something is challenging, utility doesn’t matter. Every pesky trick question answered correctly is a micro-victory. It gives off a little dopamine hit that is much more dignifying than a Facebook like. It’s more useful too, but let’s put cold utility on the back burner. What’s the point of learning pi to a thousand decimals? No more pointless than working long hours so you can buy fancy stuff without the time to enjoy it. Fun is the glue that can make everything stick. My ready knowledge of song lyrics, standup comedy, and movie trivia is a Library of Congress compared to the flimsy volume that is left of my Latin and Greek vocabulary and grammar that I was forced to digest as a teenager for six years and eight hours a week. I hated every moment of it. My teachers were kind and certainly not sadists, but they embodied the old-fashioned notion that teaching is not supposed to be fun after kindergarten. That’s a colossal error and in complete denial of the playful nature of human beings, not only children. Don’t make the same mistake. Don’t cram for the OCP unless you can make it fun. I realize I haven't given many practical examples in this post. I'll save those for next time because I'm still compiling them. Pun intended.
What a year to be a developer. As organizations rush to adopt more automated technologies driven by low code, generative AI, and other fast-moving innovations, developers accustomed to more traditional hardcoding practices will face increasing disruptions to set practices. But the transition will repay a willingness to change with significant dividends: developer automation promises superior efficiency, developer experience, and accelerated time-to-market with new application features and iterations. Capturing this automation opportunity will allow more developers to access powerful emerging technologies, eliminating requirements for specialized expertise and other cumbersome barriers of traditional manual coding. Backed by this new automation, any developer will be equipped to easily harness advanced AI, IoT, blockchain, big data, and other capabilities in their applications. Automation Efficiency and the Developer Experience Automation stands to transformatively improve developer experience, which will go a long way in winning wary developers over to the idea of accepting new practices. Leveraging low-code application development is fast becoming a standard route to automation for dev teams. With low-code development environments, developers leverage pre-packaged code modules, assembling them like building blocks within a drag-and-drop UI to build complete applications. With low code, developers can eliminate the tedious manual work common to traditional application development, replacing block-and-tackle tasks with a far more automated process. The full benefits of low code give developers a lot to like, including reduced frustration and errors, as well as greater speed, agility, and focus on the interesting feature development that developers get excited about. That superior automation-driven developer experience will give enterprises a leg up in recruiting and retaining developer talent as well, serving as a beacon to attract top prospects. Low-code automation can accelerate application development many times over compared to traditional methods (minimum 10x, in my experience, with the Interplay platform at Iterate.ai). That new efficiency frees developers to deliver application improvements more quickly, and to keep current with fast-shifting market needs. Thus, developer automation drives competitive differentiation on top of a superior developer experience. Low-code modules abstract capabilities derived from AI/ML, big data, IoT, voice, blockchain, and APIs, enabling developers with no specific hard-earned expertise to harness those technologies with plug-and-play simplicity. Currently, the most exciting development in automation technology is generative AI, which allows teams to expedite development processes, especially in tandem with low code. As an example, GitHub Copilot augments developer efforts with suggestions and assistance throughout the coding process and the ability to generate code automatically. Further generative AI tools like GPT-4, the conversation and search engine, can solve coding questions and deliver valuable developer training and support, even for developers leveraging low code. Developers' Roles in an Effective Automation Strategy Amid this positive talk about automation’s benefits, let’s be clear: the disruptive shift toward automation requires developers to accept significant changes. To unlock those productivity gains, developers must undergo their own transformations, building more valuable skillsets around automation to better use development and deployment tools, work with data and more. Enterprises will be required to evolve as well to keep pace competitively as automation sweeps development practices forward. In the scramble to complete digital transformations and harness new tools, hardware, data capabilities, and appropriate security, some organizations will thrive while others fall behind. That said, embracing automation and capitalizing on its competitive advantages and developer benefits is far, far better than the alternative of being left in the past. Approached strategically, developer automation ought to remove block-and-tackle application setup, error mitigation and other busywork from developers’ plates, instead enabling a focus on high-value work such as innovative feature development. An effective automation strategy should also anticipate rising data complexity, and meet it with investments in reliable data infrastructure and attention to data integrity. Robust data capabilities will serve as a strong foundation for AI/ML and related task automation peripheral to development, such as data entry and process handling. An automation strategy must also adapt to changing technologies and business priorities, to ensure continued access to valuable innovations and new processes. The right strategy will also enable developers to utilize automation and tooling across an expanding set of use cases and incorporate automation-driven features in their applications. Developer Automation and Data Accessibility Empowering developers to utilize automation within applications largely hinges on data access. Applications leveraging IoT, computer vision, and similar features must leverage vast quantities of data in real time. Achieving that data access means implementing infrastructure, organizational support and processes that enable efficient data collection, rapid growth, and scalability, and continuous optimization via feedback loops. Enterprises pursuing this data-driven application development have much to gain from low-code automation. Case in point: the world has only 60,000 trained data scientist engineers, and only 300,000 AI engineers. Organizations reliant on traditional hardcoding cannot achieve the competitive differentiating features they have their sights on, such as contextual responses to customer feedback, without first winning the competition to hire and retain these experts. In contrast, those that enlist low-code automation equip their existing development teams to fully harness advanced data, AI, and automation capabilities using abstracted code modules. The Automation-Backed Developer Slow and limit-bound traditional hardcoding is now overdue for replacement by automation that accelerates development to a rapid pace, transforms developer experience, and removes barriers to today’s most advanced technologies and data utilizations. Automation-backed developers will be expected to respond to market needs and deliver iterative application improvements and features at a rapid clip, and be fully empowered to do so.
In a Nutshell We work hard to improve our tech and soft skills. But our character traits, our mindset - we take it for granted. An innate, fixed part of who we are. And thus, we let it run on autopilot. This way of thinking is harmful. Not only is your mindset critical for a successful software development career (maybe even more than your skills), but it's also under your control. True senior devs acknowledge that character traits are malleable, are self-aware of their mindset, and deliberately work on it. This is the superpower that makes them stand out from the crowd and accelerates their career. In this post, I'll discuss the 10 most critical traits of a successful developer, why they are important, and share a few tips on how you can shape them. Three Things You Need To Succeed as a Software Developer Professional software development is a complex discipline that requires a diverse set of abilities to succeed. We can group them into three main categories: Technical Skills This is the most obvious group. To be a successful developer, you need to be good at your trade: programming languages and frameworks, clean code principles, architecture, testing, debugging, and so on. You also need to be skillful with your tools: version control, command line, and IDE. However, technical skills alone won't get you far in a professional, team-based setup. That's why you also need soft skills. Soft Skills To be able to work on big, commercial projects, you need a solid set of soft skills: teamwork, communication, project management and planning, remote work, self-organization, learning, personal productivity, and similar. Many of us find these skills less fun than the technical ones, and thus we often neglect them - so they can already separate solid, professional developers from the crowd. But at least we acknowledge them. There's also a third category, though, which is equally important but flies under the radar of almost all, even the otherwise good, developers. Mindset Mindset is kinda similar to soft skills, but the "skills" it's comprised of are more fundamental, core traits: curiosity, patience, empathy, grit, adaptability, and so on. On some level, we know these traits are important for a successful software development career. But because they seem an innate, fixed part of our personality, we do not attempt to deliberately learn them in the same way we learn project management or personal productivity techniques. This makes us blind to a huge and important part of our skillset and can badly hurt our progression. Why Being Blind to Your Mindset Can Jeopardize Your Career Software development is a complex, sometimes frustrating, and insanely fast-moving field. Learning, growing, and staying relevant as a developer (and not burning out in the process) requires a specific mix of character traits. At the same time, it's a 100% team game. Lone-wolf hackers have no place in professional software development, no matter how good they are technically. And functioning well in a team requires another, seemingly opposite, set of traits. Mindset is something different than skills. Understanding Agile development principles is not the same as being adaptable and open to frequent change. And knowing the debugging techniques is not the same as having the curiosity and persistence to actually enjoy chasing the problem for several hours without getting burned out or discouraged. But while these traits may seem fixed, and innate to our personality, they are equally learnable as skills. Without understanding what traits are critical for a programmer and deliberately honing them over time, you'll never reach your full potential. True senior developers know this, are self-aware of their mindset, and continuously work on improving it. This is the differentiator, the superpower that separates the best developers from the crowd. Ok, but which traits are the most important? Let's dive a bit deeper into the details. Ten Traits of a True Senior Developer 1. Curiosity If I had to summarize in one sentence what software development is about, I'd say it's about learning. You need to stay up-to-date with constantly evolving technologies and software development processes. You need to learn the deep details of complex business domains (several ones throughout your career). Analyzing and clarifying requirements is learning. Research is learning. Performance optimization and debugging - in other words, poking deeply into the inner workings of code - is a kind of learning as well. Software development is also about working with people (and for people). You'll be working on cross-functional teams with a diverse set of people from different specializations and backgrounds (nowadays, in the age of remote, most probably from multiple countries and cultures). You'll have to understand "business." You'll have to understand and solve user pain points and problems. Without a healthy dose of curiosity, you'll not only be less effective at all those things, but you'll also burn out pretty quickly. 2. Affinity for Problem-Solving Software development is a giant puzzle — an infinite stream of problems to solve. The reason you are hired as a developer is to solve your company's and customers' problems. To do this, you need to solve organizational problems (how to function as a team, how to organize your work, what processes to use) and technical problems (logic, architecture, performance, and so on). These problems consist of even smaller, nested problems, down to atomic problems like how to design and name a particular function or unit test. If you don't enjoy solving such puzzles, if you don't have a knack for breaking down and untangling problems, your software development career will be a struggle. 3. Patience Becoming a true senior developer requires years of deliberate practice. You'll also experience a lot of setbacks along the way. Developing your career is rewarding but also a slow and sometimes painful process. To achieve excellence and get to the top, you must be ready for lifelong dedication. And this requires a lot of patience. Patience is also critical for a lot of things adjacent to our job: handling technical support, working with not-very-tech-savvy users, and coping with organizational bureaucracy. Plus, it's a great problem-solving aid. And you won't sustain working in such a fast-moving, constantly changing industry as tech without patience. 4. Grit (In Just the Right Amount) Software development requires a lot of persistence. Hunting bugs. Deciphering poorly documented APIs and libraries. Untangling legacy code. Tracking down performance bottlenecks. Even simply sustaining a deep focus for extended periods of time. You'll struggle, fail, and get stuck, and get frustrated a lot — no matter how senior you are. And you'll need a lot of grit to plow through and not get burned out. But you also need to understand what's the right amount of grit. What's the sweet spot between unproductively banging your head against the wall for hours and days vs. constantly disrupting your team by requesting assistance immediately when you hit even the smallest bump in the road? 5. Emotional Intelligence Software development revolves around people and teams. You'll work very closely with your colleagues at an individual level: pair programs, debug together, and review their code. You'll also work with them in a team setup: brainstorm, plan, and make decisions as a group. And this collaboration is messy: your work will overlap or conflict, and you'll have different opinions. You'll negotiate your roadmap with management. Finally, to build a great product, you'll have to put yourself in your users' shoes. On top of that, all these people come from diverse backgrounds, both technical and non-technical. They are passionate. They have strong opinions. They may sometimes have difficult characters. And your success as a developer depends on how well you can build rapport with them. Without high emotional intelligence, it'll simply be impossible. 6. Ability To Keep Your Ego in Check Software development (and working in a team in general) is a balancing act. On one hand, you're hired for your expertise. You're expected to have strong opinions and to guide less tech-savvy or more junior people. On the other hand, you'll work with equally experienced and opinionated teammates, who will challenge your point of view and with whom you'll have to make group decisions. Your ego will often get hurt in the process. You must be able to keep it in check - but without getting withdrawn and disengaged. You must be opinionated but not zealot. Have a strong point of view but hold it weakly, be open to getting convinced otherwise. You must be ready to defend your opinion but also know when to let go, to not be a condescending, brilliant jerk. You need to respect the team, business, and customers. Be able to disagree but commit. And gracefully take constructive (and even purely negative) feedback. Otherwise, you won't be able to effectively work in a team. 7. Adaptability Everything in software development is moving so fast. Technologies are constantly changing. New methodologies get popular. Companies pivot. Throughout your career, you'll also be changing projects, teams, companies, and business domains. Even a single project is a constant act of inspecting and adapting (especially in agile approaches). And your team will constantly self-reorganize, too. Most people are allergic to change. Change is hard. It's uncomfortable. It's stressful. Being adaptable and open to change will instantly set you apart. It will not only let you climb to the top of the seniority ladder, but it will also let you stay there for a long time. 8. Reliability I'm repeating it ad nauseam, but software development is a team game. Your colleagues, manager, and company - they all count on you to do your part. Nobody will ever consider you a true senior developer - no matter your tech expertise - if they can't rely on you to take care of your work and deliver on your promises without having to be micromanaged. It doesn't mean you can never make any mistakes. Failures happen. And the best companies see them as valuable learning opportunities. But to enable this, you need to be able to pick up a dropped ball, gracefully recover, and be trusted to learn from your failure and not repeat it in the future. 9. Pragmatism Professional software development is an art of tradeoffs. You constantly need to compromise between development speed and quality. Balance new and promising with proven and stable. Walk a thin line between under- and over-engineering. To succeed in professional software development, you need to be very pragmatic. You need to understand that nothing is black and white and that no principle or pattern holds true in every situation. You must have great intuition for making tradeoffs between different approaches, technologies, and solutions; feel comfortable cutting corners but have a good sense of how much. 10. Positive Outlook Your life as a programmer is not all sunshine and rainbows. You'll meet annoying customers. Face tight deadlines. Your project may get canceled. You may disagree with your team or management but still have to commit and execute. You'll also work with ugly code. (Yes, every codebase has good and bad parts, even at the top companies like Google.) You'll get tired, frustrated, and upset. If you let negativity take over, if you start criticizing and complaining, you'll not only demotivate yourself, but you'll also kill the morale of your team - which won't take your career very far. You need to be biased toward the positive. Be optimistic and cheerful. Always look for a silver lining. Be the person who rallies their team, kills the bad mood, and restores morale. It'll not only get you noticed and promoted, but it'll also make your career more pleasant and sustainable. Bonus: A Few Tips on How to Deliberately Shape Your Mindset Skills, obviously, can be trained - both soft and technical ones. You can get better at debugging as well as at communication. But what about such seemingly innate traits like curiosity or a positive outlook? Can you really train yourself to be more optimistic or curious? Yes, you can! This is a vast topic, worth several books ("Mindset" by Carol S. Dweck is a great starting point). But let me quickly share a couple of tips: Acknowledge that your mindset is not fixed and that your traits are malleable. Build self-awareness. Observe how you react in different situations. Try to understand what makes you feel in a particular way (curious vs. bored, positive vs. grumpy, eager vs. defensive). Retrospect your behavior from a perspective of a day or two. Was your opinion really pragmatic, or was it your ego talking? How could you have acted differently? Prepare in advance. Pre-plan up-front how you'll behave next time in a similar situation and identify the right trigger that will remind you about your plan when the time comes. Expose yourself to situations that will let you exercise and strengthen desired traits. Actively look for such opportunities. Focus on a single trait for some time. It will make it easier to find opportunities to exercise it and to increase your self-awareness of this trait. Reframe. Be conscious and deliberate about how you talk to yourself. The idea isn't stupid; it's surprising. That shiver isn't anxiety; it's excitement. The problem isn't frustrating; it's interesting. Enlist help. Don't shy from asking your colleague or manager for feedback - or even to become your "accountability partner" who will catch and point out your unwanted behavior. I also encourage you to learn a bit about cognitive biases and habit forming. This is a well-developed discipline with a huge body of knowledge and pretty fun to explore. Bottom Line If you want to be a true senior developer, you have to be a complete one. You must have the right mix of tech skills, soft skills, and character traits, and you can't ignore any part of this trio. Character traits are the most tricky part. Most developers neglect them because they are either not self-aware of them, don't know which ones are important, or don't believe they are malleable. But if you can overcome this flawed way of thinking — if you can change your mindset — you can turn it into a superpower that will elevate your career and make you truly stand out.
Development practices are constantly changing, and as testers, we must embrace change. One of the changes that we can experience is the move from monthly or quarterly releases to continuous delivery or continuous deployment. In addition, this move to continuous delivery or deployment offers testers the chance to learn new skills. A project that makes monthly or quarterly releases has a familiar rhythm, and the team builds toward the release date. The testers must test all the cards and conduct manual regression testing. Every test in the manual scripts needs to be executed, and possibly a report needs to be made on the results of these tests. Once the release has been made, it is possible that there will be bugs that were in the release that need to be fixed. The testers will also need to start running the same manual regression tests on the next release and report on them again. Testing for monthly or quarterly releases is a repetitive process. This process has been compared to the Greek myth of Sisyphus, who had to roll a stone to the top of a hill, and then when the stone rolled to the bottom of the hill, he had to roll it to the top of the hill again. Continuous delivery can be defined as “when all developers are working in small batches on the trunk, ….and when the trunk is kept in a releasable state, and when we can release at the push of a button”. A team that I worked with moved from making monthly releases to continuous delivery. The team kept the main branch in a state in which it could be deployed if needed, and the team made weekly releases. Continuous deployment can be defined as when, in addition to the practices that support continuous delivery, “we are deploying good builds into production on a regular basis through self-service (being deployed by Dev or by Ops). A team practicing continuous deployment deploys to production each time code is merged to the main branch. Teams that practice continuous delivery or continuous deployment use small batch sizes. That means that the “batch” of code that is deployed is small. “The theoretical lower limit for batch size is single piece flow, where each unit is performed one at a time”; this is what happens in a continuous deployment where each merge to the main branch is deployed to production. Teams that practice continuous delivery and continuous deployment try to create a flow of work at a sustainable pace and so should “enable and inject learning into daily work.” On the other hand, teams that make monthly releases build towards the release and so can not create this constant flow of work at a sustainable pace. When a team moves from monthly releases to continuous delivery or continuous deployment, a change that occurs is that there is no release candidate. Testing is not done on a release candidate; instead, it is done on feature branches that have been taken from the main branch, and when they are merged back into the main branch, they must be ready for release to production. The main branch is kept in a state where it can be released to production. For the tester, this means that the testing on a feature branch has a different pattern than that on a release candidate. When testing on a feature branch, you need to be confident that the new feature or fix in the feature branches does what it is supposed to do and that it has not caused any regression. When testing a monthly release, you can have time to execute manual regression tests, but if the main branch is to be kept in a state where it can be deployed to production, this is not possible. Regression testing needs to be automated if you are using continuous delivery or continuous deployment. Regression testing on monthly releases often includes running a large number of manual tests; however, if your team is using continuous delivery or continuous deployment, regression testing is usually automated via continuous integration. Continuous integration (CI) “means that every single time somebody commits any change” to the code, the change is integrated into the codebase. This entails running automated tests before and after merging the code into the main branch. This provides an opportunity for the tester to learn how to understand CI. The tester must understand CI, including what tests run as part of CI. There will always be gaps in the tests that run on CI. If the tester knows what the gaps in CI are, they can work out how to automate tests to fill them and execute manual tests to cover the gaps when this is required. Making mind maps of the tests that are automated can help identify gaps in the automated tests. Testers can also get involved in automating regression tests themselves, and in this way, testers can help to prevent bugs rather than find them. There are great free resources such as Test Automation University, LambdaTest Certifications, and Exercism which can help testers gain the skills they require to automate tests. There are also lots of resources to learn how to use javascript to aid testing. That regression testing is automated creates time for the tester that they can spend on exploratory testing. Exploratory testing is a powerful way to uncover issues, and so it will help the projects that the tester is working on. Having extra time to do exploratory testing will also help the tester develop their exploratory testing skills. Projects that use continuous delivery and continuous deployment also tend to have a microservices architecture. Microservices are services that have independent testing and deployment, and each service is simple. The tester has the opportunity to learn about the microservices, and ways to do this include talking to the developers, studying any architecture diagrams that exist, reading the readme files for each service in GitHub, and attending the developers’ meetings. Testers can build their relationships with developers by helping them test their code. In addition, the tester can share their knowledge of testing techniques, such as boundary value analysis, with developers, as this will help them test and produce better quality software. Release processes for monthly releases can involve a certain amount of pain for testers. Sometimes we are asked to take the responsibility of making the release decision even though the tester is often a junior member of the team; other times, testers have to take part in large committee meetings of stakeholders that decide if the software can be released. Release processes for continuous deployment and continuous delivery should be automated; this means that making releases does not put pressure on the tester. The tester’s input to the release in continuous delivery and continuous deployment is their testing; this means that we can focus on our testing and learn new testing skills. Development teams are not autonomous; they are open systems whose work affects other teams and is affected by other teams. This is systems thinking, and systems thinking contributed to continuous delivery and continuous deployment. Testers can learn to use systems thinking to enhance their testing and support their team. This can help the tester think beyond their role to understand what other systems are affected by their team’s work and what systems affect their team’s work. One of the lessons of systems thinking is that everyone shares responsibility, so no one person should be blamed when something goes wrong. This view should also be at the heart of every agile and lean development team that implements continuous delivery or continuous deployment. This is something that testers should learn from and take to heart. When there is a failure, we need to learn from it and not blame someone. Teams that make monthly releases can find that after each release, there is a flurry of activity fixing regression bugs that were deployed in the release. This does not happen with teams that practice continuous delivery or continuous deployment. Releases will be deployed multiple times a day using continuous deployment, and software will be deployed regularly with continuous delivery. These regular releases give the development team an opportunity to recover from bugs and incidents quickly, as fixes can be deployed quickly. Once a fix has been deployed, the tester can offer to take the lead in doing root cause analysis to find the root cause of the bug or incident. The tester can learn to use the Five Whys and Ishikawa Diagrams to do root cause analysis. Testers can also support the work of their teams by producing metrics that help the team measure their quality improvement. DORA metrics were identified by the Accelerate State of DevOps survey report by DORA. These metrics are designed to help teams that practice continuous delivery and continuous deployment find areas for improvement and know how they are doing. These are different from the metrics for monthly releases as they are not about how many bugs have been put into production but are about how quickly a team recovers from an error. Continuous delivery and continuous deployment offer testers opportunities to grow and learn new skills, so testers should embrace the opportunities when their teams move to continuous delivery and continuous deployment.
I taught myself to code from scratch. I just started writing in Basic on my Sinclair when I was in kindergarten and “got it.” I later picked up the books and courses easily without that initial study period of formal training. When I got to advanced academic materials, I already knew pretty much everything. As a matter of fact, I never learned much from teachers. I learned to read mostly on my own and followed through with most disciplines. This isn’t a “brag” because the reason for that is a learning disability. It’s hard for me to understand teachers and communicate with them. This was a problem in my childhood but became a bigger problem when I started to teach programming in a local computer lab at 16. I was a terrible instructor. Typically we would mimic the skills of a good role model when performing a task. I didn’t have any good teachers. At least not good for my learning disability. I kept teaching on and off for over a decade. It wasn’t because I was good (I wasn’t), but you couldn’t tell that from the feedback I received. Most students gave me high ratings. Why did they? The main reason was that I was knowledgeable and made an effort. The students who “didn’t get it” would end up blaming themselves or making an extreme effort to understand. This dragged on for a while until I faced a class in which I got a bad review. It stung, and I initially rejected the notion that I could be the one at fault. Eventually, it sunk in. I took that to heart and improved. I’m still not a great teacher. One of my shortcomings is my thought process isn’t visual enough, so I don’t communicate as visually as most students need. I’m working on that, and it’s an ongoing process. People like me who aren’t natural teachers need to overcompensate in other departments. One of the biggest tricks up my sleeve is my approach to learning. I teach theory in short bursts while focusing on building something cool. Why Do We Have Hello World? As a teacher, I usually taught advanced classes. Those are the “easy” classes to teach. In these classes, the students already have a decent baseline. If they don’t understand something, they can check, and they don’t get intimidated or ashamed because they missed something the teacher mentioned. A beginner class is an enormous challenge. If I explain something only once or I forget to mention something, I might lose the entire class. People might be missing basic knowledge, and if I explain it too much or too thoroughly, some students might get bored and lose interest. You lose if you do, and you lose if you don’t. There’s very little “winning” in this area. That’s why we have the “hello world.” It isn’t just to show how the language looks or how the tools function. It’s a “win.” Once we compile and run a version of “hello world” we accomplish “something,” and that is the most important part. It gives students the motivation to keep going and double down. Even if I missed something when teaching, they might make the extra effort to go back and learn or ask a question if they feel a sense of accomplishment. A few years ago, gamification was a “hot trend.” Thankfully it’s out of vogue. I never liked it. Stack overflow is great (that statement doesn’t condone the actions of overzealous rogue moderators), but what I liked about stack overflow wasn’t the gamification. It’s the accomplishment that rewarded that. I enjoy the points or badges, not because they are points or badges. I enjoy them because I earned them by giving a great answer. This is the core of the matter and what most learning experiences miss: winning. In It To Win I see a syllabus like this in practically every “beginner” oriented course: Hello world Variables If statements Loops … We accomplish one thing. Then it’s “study”; there’s no “reward” for successful accomplishment or something measured that would trigger excitement. I get why we teach that way… people need the theory. But is that theory sinking in if we learn it sequentially? When we build an ongoing demo and constantly improve it, we learn the theory while continuously accomplishing such goals. Better yet, we can intertwine it with a narrative story of developing an application or a game that helps memorize and form a narrative trail. There’s one downside: the tutorial sucks as a reference since the ideas are mixed within an additional narrative. But the advantage is the “show, don’t tell” approach. I love encapsulation. It’s wonderful. Don’t explain it. Show me why the code looks bad if I don’t use it. Don’t explain every type and waste my time. Show me how to build stuff, and then go back and refine it to explain the nuances involved in practical terms. This is the exact approach I tried to take with my Java Basics free course. The idea is that we can build a Wordle clone without knowing anything and slowly refine it into a real-world application. Yes, we need to understand some basics before we can write something useful. But the bar is very low. Better yet, we need to make flawed and imperfect code so we can show how to improve and fix it. Achievement Unlocked When we say gamification, this is what it really means; to achieve something meaningful and not merely satisfy a metric, a company needs to further push a user through a funnel. This applies to teaching coding skills and to your SaaS. Quite a few SaaS companies still show pointless “awards” and badges to get us engaged. Even kids don’t buy that nonsense in their iPad games. A reward matters only when you made an actual effort and should signify something with meaning. There’s harm in those nonsense awards as they reduce your credibility. When you have an important milestone, that award won’t matter as much. In any product, we need to divide the steps into achievable hops where something meaningful is accomplished before moving to the next step. An important aspect is milestone communication. A person needs to understand the goals, short and long-term. When I teach, I start a lesson by discussing what they will know by the end of the lesson: why should you pay attention? When I start the course I need to discuss the ending. What you will know and ideally show what we will build. I didn’t do this for the videos in the Java Basics course since I’m building them as I go along. That is not an ideal scenario. But I might revisit the first video in the series and redo it once I’m done. This gives us the motivation to work towards a clear goal.
As AI technology continues to evolve, it is creating a vast array of job opportunities that were once unheard of. While traditional AI roles such as data scientists, machine learning engineers, and AI researchers are widely recognized, there are several lesser-known careers that are equally important in driving change in the tech industry. In this article, we will explore 21 hidden careers in the AI revolution. These hidden careers in the AI revolution are becoming increasingly vital as AI technology becomes more pervasive in various industries. From AI psychologists who understand the human impact of AI to AI knowledge engineers who ensure AI systems have the necessary information to perform their tasks, these roles are critical to creating AI systems that are effective and ethical. Moreover, the rise of AI in the industry has created a need for AI professionals who can develop and implement AI tools and solutions tailored to this niche market. This highlights the versatility of AI professionals and the diverse range of applications for this technology. Here are 21 hidden careers in the AI revolution that are driving change in the tech industry. AI Ethicist: An AI Ethicist is a professional who specializes in the ethical considerations and implications of Artificial Intelligence (AI). They work to identify and address potential ethical issues that may arise from the development and deployment of AI technologies. This includes topics such as bias, privacy, safety, and transparency. AI Ethicists collaborate with various stakeholders, including developers, policymakers, and end-users, to ensure that AI systems are developed and used in a responsible and ethical manner. Their work is essential in shaping the future of AI and ensuring that it aligns with the values and principles of society. AI Psychologist: An AI Psychologist is a professional who specializes in the intersection of artificial intelligence and human psychology. They work to develop and implement AI technologies that can enhance mental health and well-being, as well as improve the diagnosis and treatment of psychological disorders. AI Psychologists use machine learning algorithms and natural language processing to analyze large amounts of data and provide personalized interventions and recommendations to individuals. They collaborate with other mental health professionals to ensure that AI technologies are used in an ethical and responsible manner and that they align with the values and principles of the mental health profession. AI Business Strategist: In AI Business, my role is to help organizations leverage artificial intelligence to improve their operations and gain a competitive advantage. This involves identifying areas where AI can be applied, developing AI strategies, and overseeing the implementation of AI solutions. One of the key benefits of AI is its ability to automate repetitive tasks and analyze large amounts of data quickly and accurately. This can lead to increased efficiency, improved decision-making, and cost savings for businesses. However, implementing AI solutions also requires careful consideration of ethical and privacy concerns, as well as ensuring that employees are trained to work alongside AI systems. As an AI business strategist, I work to ensure that these issues are addressed and that AI is used in a responsible and effective way. AI Language Specialist: As an AI language specialist, my focus is on developing natural language processing (NLP) technologies that enable machines to understand and generate human language. This involves working with large datasets and using machine learning algorithms to teach machines to recognize patterns and understand language structures. NLP technologies have many practical applications, from chatbots and virtual assistants that can understand and respond to human queries to sentiment analysis tools that can analyze customer feedback and identify areas for improvement. One of the biggest challenges in NLP is developing systems that can understand the nuances of human language, such as sarcasm, irony, and metaphor. As an AI language specialist, I work to develop systems that can accurately interpret these nuances and generate appropriate responses. Overall, my goal as an AI language specialist is to create technologies that enable machines to communicate with humans in a natural and intuitive way, ultimately making our interactions with technology more seamless and productive. AI Trainer: An AI Trainer is a professional who specializes in training and developing artificial intelligence systems. They work to design and implement machine learning algorithms that can learn and adapt to new data over time. AI Trainers also work to identify and address potential biases or inaccuracies in AI models, ensuring that they are accurate, reliable, and effective. They collaborate with other professionals, such as data scientists and software engineers, to develop and test AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in shaping the future of AI and ensuring that it aligns with the needs and expectations of society. AI Translator: An AI Translator is a professional who specializes in developing and implementing artificial intelligence technologies to facilitate communication between people who speak different languages. They work to design and implement machine learning algorithms that can accurately translate speech and text in real time. AI Translators use natural language processing and other advanced techniques to identify nuances in language and ensure that translations are accurate and culturally appropriate. They collaborate with other professionals, such as linguists and software engineers, to develop and test AI translation systems across various industries, including travel, hospitality, and international business. Their work is essential in breaking down language barriers and facilitating global communication. AI Infrastructure Architect: An AI Infrastructure Architect is a professional who specializes in designing and implementing the technological infrastructure that supports AI systems. They work to develop and optimize the hardware and software components that are required for running large-scale AI applications. AI Infrastructure Architects also work to ensure that AI systems are secure, reliable, and scalable and that they can handle large amounts of data in real time. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in enabling the growth and advancement of AI technology. AI DevOps Engineer: An AI DevOps Engineer is a professional who specializes in the development and deployment of artificial intelligence systems. They work to streamline the process of building, testing, and deploying AI applications, ensuring that they are efficient, reliable, and scalable. AI DevOps Engineers also work to identify and address potential issues with AI systems, such as performance bottlenecks and security vulnerabilities. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in ensuring that AI systems are optimized for performance and reliability. AI Data Analyst: An AI Data Analyst is a professional who specializes in analyzing and interpreting large amounts of data using artificial intelligence technologies. They work to develop and implement machine learning algorithms that can identify patterns and insights in complex data sets. AI Data Analysts also work to ensure the accuracy and validity of data used in AI systems and to optimize data processing and storage methods. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in enabling data-driven decision-making and unlocking the potential of AI technology. AI User Experience (UX) Designer: An AI User Experience (UX) Designer is a professional who specializes in designing user interfaces and experiences for artificial intelligence systems. They work to create intuitive and user-friendly interfaces that enable users to interact with AI systems in a natural and intuitive way. AI UX Designers also work to optimize the performance and usability of AI systems, ensuring that they are accessible to users of all skill levels. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in creating engaging and effective AI applications that can be easily adopted by users. AI Product Manager: An AI Product Manager is a professional who specializes in managing the development and launch of artificial intelligence products. They work to identify market opportunities for AI products and define product requirements based on customer needs and market trends. AI Product Managers also work to ensure the successful launch and adoption of AI products by developing marketing and sales strategies, managing product pricing and distribution, and providing customer support. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in enabling businesses to leverage the potential of AI technology and stay competitive in the market. AI Security Specialist: An AI Security Specialist is a professional who specializes in ensuring the security and privacy of artificial intelligence systems. They work to identify potential security risks in AI systems and implement measures to protect them from cyber-attacks and other threats. AI Security Specialists also work to ensure the confidentiality of sensitive data used in AI systems, such as personal information and financial data. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in ensuring the safety and reliability of AI technology and preventing potential security breaches. AI Data Steward: An AI Data Steward is a professional who specializes in managing the quality, consistency, and security of data used in artificial intelligence systems. They work to ensure that data used in AI systems is accurate, complete, and up-to-date, and they implement measures to protect it from cyber threats and unauthorized access. AI Data Stewards also work to improve data governance practices, such as data lineage and data classification, to support the ethical and responsible use of AI systems. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in enabling businesses to leverage the potential of AI technology while ensuring the quality and integrity of the data used to power AI systems. AI Human Enhancement Specialist: An AI Human Enhancement Specialist is a professional who specializes in using AI technology to enhance human capabilities, such as memory, vision, and decision-making. They work to develop and implement AI-powered tools and devices that can augment human abilities, such as brain-computer interfaces and neuroprosthetics. AI Human Enhancement Specialists also collaborate with healthcare professionals to develop AI-powered treatments for conditions such as paralysis, stroke, and Alzheimer's disease. They are experts in both AI technology and human physiology, and their work is essential in unlocking the full potential of AI technology in improving human health and performance. AI System Architect: An AI System Architect is a professional who designs and implements complex AI systems that integrate multiple technologies and data sources. They work to develop a comprehensive understanding of the business needs and objectives and then create a blueprint for the AI system architecture that will meet those needs. AI System Architects also work to identify and address potential technical challenges, such as data integration and security, and ensure that the AI system is scalable and adaptable to future changes in the business environment. They collaborate with other professionals, such as data scientists and software engineers, to implement and maintain the AI system, and their work is critical in enabling businesses to leverage the full potential of AI technology for competitive advantage. AI Process Engineer: An AI Process Engineer is a professional who specializes in using AI technology to optimize business processes and workflows. They work to identify opportunities for automation and streamlining of processes and then use AI tools and techniques to design and implement more efficient and effective processes. AI Process Engineers also monitor and analyze process data to identify areas for improvement and make data-driven recommendations to enhance performance. They collaborate with business stakeholders, such as operations managers and IT professionals, to ensure that the AI-powered processes are aligned with business objectives and requirements. Their work is essential in driving organizational efficiency and agility through the application of AI technology. AI Entertainment Producer: An AI entertainment producer utilizes AI tools and techniques to create and produce various forms of adult entertainment, such as Images, movies, TV shows, and video games. They can employ AI to generate scripts, characters, or music or to enhance the overall production process by automating tasks like editing and post-production. AI entertainment producers can help to streamline production timelines, reduce costs, and create new and innovative forms of entertainment that leverage the latest AI technology. AI Model Evaluator: An AI Model Evaluator is a professional who specializes in assessing the performance and reliability of AI models. They work to ensure that AI models are accurate, unbiased, and effective in solving specific business problems. AI Model Evaluators use a variety of tools and techniques to test the AI model's performance against relevant data sets and real-world scenarios. They also work to identify and mitigate potential risks associated with the use of AI, such as privacy violations or unintended consequences. AI Model Evaluators collaborate with data scientists and other stakeholders to make data-driven decisions about the design and implementation of AI models, and their work is critical in ensuring the responsible and ethical use of AI technology. AI Knowledge Engineer: An AI Knowledge Engineer is a professional who specializes in creating and managing the knowledge base for AI systems. They work to ensure that AI systems have access to relevant and accurate data and information, which is necessary for them to function effectively. AI Knowledge Engineers use a variety of tools and techniques to build, organize, and maintain the knowledge base, such as natural language processing, machine learning, and semantic web technologies. They collaborate with other AI professionals, such as data scientists and software developers, to design and implement AI systems that can effectively process and apply knowledge to real-world problems. The work of AI Knowledge Engineers is critical in ensuring the success of AI systems in various industries, from healthcare to finance to retail. AI Domain Expert: An AI Domain Expert is a professional who has deep knowledge and expertise in a specific domain or industry, such as healthcare, finance, or transportation. They work with AI teams to provide domain-specific insights and guidance, helping to ensure that AI systems are designed and implemented to meet the unique needs and challenges of their industry. AI Domain Experts work closely with AI developers, data scientists, and other professionals to develop and test AI models, ensuring that they are accurate, reliable, and aligned with industry best practices. They also play a key role in communicating the potential benefits and limitations of AI systems to stakeholders, such as policymakers, regulators, and customers. The work of AI Domain Experts is essential in enabling the successful deployment and adoption of AI technology in various industries. AI Solution Architect: An AI Solution Architect is a professional responsible for designing and implementing complex AI systems that meet the needs of their clients. They work with clients to understand their business requirements and design AI solutions that are efficient, scalable, and cost-effective. AI Solution Architects use their expertise in AI technologies, software engineering, and system integration to design and develop AI systems that can process and analyze large amounts of data, make accurate predictions, and automate tasks. They also ensure that AI systems are secure, reliable, and compliant with regulatory requirements. The work of AI Solution Architects is critical in enabling organizations to leverage the full potential of AI technology and gain a competitive edge in their respective industries. AI Education Specialist: An AI Education Specialist is an expert in developing and implementing AI-based educational programs and curricula. They design and create learning materials that incorporate AI technologies, such as machine learning and natural language processing, to enhance the learning experience. AI Education Specialists work with educators and institutions to help them integrate AI into their teaching methods and courses, and they provide training and support to ensure that educators have the skills and knowledge necessary to use AI effectively. They also conduct research on the impact of AI on education and work to develop new educational models that leverage AI to improve learning outcomes. The role of AI Education Specialists is becoming increasingly important as AI continues to transform the education industry and create new opportunities for students and educators alike. These are just a few examples of the hidden careers in the AI revolution that are driving change in the tech industry. As AI technology continues to advance, it is likely that new roles will emerge to support it. If you're interested in pursuing a career in the AI industry, it's worth exploring these lesser-known roles to find the perfect fit for your skills and interests.
There's a good reason why Java is one of the most widely used programming languages: it's very powerful and flexible. Because of its adaptability and power, it may be used in a wide variety of applications, including the development of web applications and Android applications. However, it may be difficult for newcomers to know where to begin since there is so much information out there. But worry not! You won't need to go elsewhere after reading this article. We have compiled a list of the five best Java books for beginners, each of which is simple to read and understand while yet doing an excellent job of explaining the fundamentals of the language. These books provide a complete overview of the world of Java programming, covering everything from syntax and programming ideas to more advanced subjects such as data structures and object-oriented programming. What Is Java? Java is a widely used object-oriented programming language and flexible software platform that guides billions of devices across the globe, including computers, gaming consoles, medical equipment, and a broad variety of other types of products. Java provides developers with several advantages since it is based on the syntax and guidelines of C and C++. When it comes to the development of software, adopting Java has a number of key benefits, one of the most prominent being its remarkable portability. You can use a notebook computer to develop code and then simply move that code to any device, including mobile devices, if you are using Java. Since its creation in 1991 by James Gosling of Sun Microsystems (which is now owned by Oracle), the language has remained a top option for developers all over the globe. The language was established with the objective of "write once, run anywhere,". Java allows developers to concentrate on developing cutting-edge applications without worrying whether or not their code will work correctly on other systems. Although the terms Java and JavaScript may seem interchangeable, there is a significant difference between the two. JavaScript does not need compilation, but Java does. On top of that, in contrast to JavaScript, Java may be executed on any platform. New and enhanced software development tools are being released at a dizzying rate, driving fast change in the industry. These technologies are posing a threat to businesses that were previously considered vital; nonetheless, in the middle of all this upheaval, one language has stayed constant: Java. Even more amazing is the fact that almost two decades after its creation, Java remains the preferred language for the development of application software. Developers continuously choose it above other popular languages such as Python, Ruby, PHP, Swift, and C++. Therefore, it should come as no surprise that having knowledge of Java is necessary for everyone who wants to compete in the employment market of today. The language has been around for a long time and is very popular, which shows how reliable and useful it is. This makes it a valuable tool for coders and organizations alike. How To Determine Which Java Book Is Right for You You could feel overwhelmed if you're just starting out in programming and looking for the perfect Java book, but don't worry about it! You'll find the ideal resource quickly with the help of our educated recommendations. First and foremost, you need to evaluate your existing level of expertise. If you're just starting off, it's best to read a book that lays a solid foundation for you. Give priority to writers that have years of expertise in real-world programming and a track record of being effective in teaching Java. It is helpful to read reviews written by other customers before making a purchase decision. Readability, structure, and general effectiveness of the material as a Java guide are all things that need to be investigated. Next, take into account the time and financial limits you have. Compare the advantages of purchasing a physical book vs an e-book or online course and decide if you want a full book or a short guide. Last but not least, give some thought to the way in which you take in information best. If you learn best via direct participation, you should look for a book that is packed with a wide variety of hands-on activities and projects. If you would rather take a more theoretical approach, another option is to choose a book that explores the "why" behind Java's features and the way it operates. Top 5 Java Books for Beginners 1. Head First Java Kathy Sierra and Bert Bates' Head First Java is widely regarded as the definitive introduction to the Java programming language. This book is packed with extensive knowledge of Java programming fundamentals such as classes, threads, objects, collections, and language features. The content is delivered in a visually attractive way, and the book incorporates puzzles and games to make it easier to comprehend Java programming. This book stands out from others on the market due to the fact that it contains interviews with experienced Java programmers. These programmers are kind enough to offer their expertise and tips in order to accelerate the learning process for Java beginners. In the first chapter of Head First Java, the author takes a deep dive into the concepts of inheritance and composition. These concepts provide a terrific opportunity to improve computing practices via the process of problem-solving. In addition, the reader will find helpful tools in the form of vivid charts, memory maps, exercises, and bulleted lists throughout the course of the book to assist them in comprehending design patterns. This book, which has a total of 18 chapters and covers topics ranging from basic introductions to distributed and deployment computing, is without a doubt the best resource available for beginners who are just starting out in the world of Java programming. If you can have the greatest, why go for anything else? Grab Head First Java right now to get started on your path to becoming a Java programming expert, and get ready to open the door to a world of unimaginable opportunities. 2. Java for Dummies The book Java for Dummies written by Barry A. Burd is an excellent resource for anyone who is interested in delving into the realm of Java programming. Using the book's lucid instructions, readers can learn to design their own fundamental Java objects and become experts at code reuse. This book gives a full explanation of how Java code is processed on the CPU by providing a wealth of visual aids, including useful photographs and screenshots. However, this is not all that Java for Dummies has to offer; it goes above and beyond to provide a reading experience of the highest caliber. The book is comprised of nineteen chapters, the first of which provides readers with professional advice on how to make the most of their time spent reading the book, while the last chapter provides readers with a list of the best ten websites available to Java programmers. Along the way, readers will get familiar with enhanced features and tools introduced in Java 9, acquire knowledge of approaches and strategies for integrating smaller applications into larger applications, and acquire a thorough understanding of Java objects and code reuse. The reader will be able to comfortably overcome any programming difficulty after reading this book since it also offers helpful guidance on how to easily manage events and exceptions. Overall, Java for Dummies is a book that should be read by everyone who wants to become an expert in Java programming and push their abilities to the next level. 3. Java Programming for Beginners The book Java Programming for Beginners, by Mark Lassoff, is a great way to get started in the world of Java programming. It will walk you through the fundamentals of Java syntax as well as the complex parts of object-oriented programming. By the end of the book, you will have a thorough grasp of Java SE and be able to create GUI-based Java programs that run on Windows, macOS, and Linux computers. This book is packed with information that is both informative and entertaining, as well as challenging exercises and hundreds of code examples that can be executed and used as a learning resource. By reading this book, you will go from knowing the data types in Java through loops and conditionals, and then on to functions, classes, and file handling. The last chapter of the book provides instructions on how to deal with XML and examines the process of developing graphical user interfaces (GUIs). This book provides a practical approach to navigating the Java environment and covers all of the fundamental subjects that are necessary for a Java programmer. 4. Java: A Beginner's Guide Herbert Schildt's book Java: A Beginner's Guide is widely regarded as one of the best introductions to the Java programming language. Aspiring computer programmers should make this book, which is more than 700 pages long and contains a jewel, their primary reference since it covers all the fundamentals in an easy-to-read way. This book starts out with the fundamentals of Java syntax, compiling, and application planning, but it goes fast on to more sophisticated subjects really quickly. You'll get right into practical, hands-on lessons that push you to consider carefully the fundamental ideas behind Java programming. In addition, there is a test at the end of each chapter, so you'll have lots of opportunities to put what you've learned into practice and demonstrate that you understand it. However, what sets this book different from others on the market are the helpful insights and suggestions provided by Java programmers who have years of experience. These professionals share their insights and experiences with you, allowing you to win over everything that stands in your way. They cover anything from everyday quirks to massive challenges. It's possible that Java: A Beginner's Guide is too complex for some people, but it's ideal for those who are prepared to put in the work and Google questions as they go along. So, why should you wait? With the help of this invaluable book, you can get started on your path to becoming a Java expert right now. 5. Sams Teach Yourself Java Sams Teach Yourself Java is distinguished not only by its outstanding writing style but also by its ability to enable readers to comprehend the language in less than 24 hours. Okay, maybe 24 hours is a bit of a stretch, but the fact remains that this book is the best way to learn Java quickly. The activities are broken down into manageable chunks, and the provided explanations are both thorough and easy to follow. This book walks you through the full process of building a program by breaking it down into stages that are simple to comprehend and follow you step-by-step. You'll learn how to examine the process and apply key ideas to future tasks, which will help you understand the language better overall. Having a solid understanding of the theory that lies behind Java is one of the most essential components of producing code in that language. This is where the book really shines since it makes you think about the whole process before you write a single line of code. If you do so, you will put yourself in a position where you can easily tackle even the most difficult programming problems. Sams Teach Yourself Java is a wonderful option for anybody who wants to get a deeper grasp of the language, regardless of whether they are beginners or intermediate coders. Is It Worth Learning Java in 2023? Are you considering learning Java in 2023? There is no need to continue investigating since the answer is an obvious yes. Java is quickly becoming a crucial programming language for software developers as the focus of the globe moves more and more toward mobile applications and convenience. It's been the third most popular language among employers for the past two years, and it doesn't look like it's going to slow down anytime soon. Despite the fact that the pandemic has clearly had an effect on the number of available jobs, the demand for Java developers is still considerable. In point of fact, there is a great deal of compelling reasons why you need to study Java in the year 2023. Reasons Why You Should Seriously Consider Learning Java in 2023 Java Is Friendly for Beginners Java has an open-door policy for beginners. Java is a fantastic language that will assist you in getting your feet wet in the realm of coding and navigating your way through the complex landscape of software development. In addition, since Java programmers earn a wage that is on average higher than those who program in other languages, Java is an excellent choice for new programmers to study as they extend their language skills and advance their careers. Use of Java Is Not Going Away Anytime Soon In the last few years, Java has stayed in a pretty stable situation, with at least 60,000 jobs always open. Python has made significant progress in recent years, but this has not prevented Java from becoming the dominant programming language in use today. Java has earned its reputation as the "workhorse" language of the programming industry for a good reason. When we look into the future, we can say with absolute certainty that Java will continue to be regarded as the most effective programming language for many years to come. Because of its reliability and adaptability, it is an excellent investment for any programmer or company that aims to develop systems that will stand the test of time. Therefore, you may relax knowing that Java will not be disappearing at any time in the near future. Versatile and Flexible Language Companies were confronted with a significant obstacle during the pandemic when workers were required to work from home. Because many businesses did not have the appropriate infrastructure and equipment to support remote work, their workers were forced to utilize their own personal devices, such as laptops, mobile phones, and tablets. However, the trend toward remote work began long before the pandemic and will continue even after it has passed. Good News for Those Who Code in Java Java is a very versatile and adaptable programming language that can operate on any operating system, including Mac OS, Windows, and even Android. Java allows businesses to design their own private software with the peace of mind that it will function faultlessly across all of the devices used by their workers while maintaining the highest levels of safety, security, and reliability. Java is, without a doubt, the best answer for businesses that want to keep up with the times and provide their workers with the resources they want to be able to do their jobs from any location and at any time. Strong Support From the Community Java has been around for a number of decades now and can be thought of as one of the oldest programming languages that are still in use when compared to its competitors. Many developers utilize Java for many challenges. There is a good probability that the solutions to the majority of the issues will already be accessible, since the method to finding them may have been tried and proven before. Additionally, there are a large number of communities and groups on the internet and social media, respectively. The other developers and newcomers to the field will find that their peers in the community are eager to provide a helping hand and find solutions to the problems they are experiencing. Multiple Open-Source Libraries Are Available for Java The materials included in open-source libraries may be copied, researched, modified, altered, and shared, among others. There are a number of open-source libraries in Java, including JHipster, Maven, Google Guava, Apache Commons, and others, which may be used to make Java development simpler, more affordable, and more efficient. Java Has Powerful Development Tools Java is more than just a programming language; its Integrated Development Environments (IDEs) make it a software development juggernaut. Developers have all the resources they need to produce top-notch apps thanks to industry-leading tools like Eclipse, NetBeans, and IntelliJ IDEA. These integrated development environments (IDEs) provide a wide variety of features, ranging from code completion and automatic refactoring to debugging and syntax highlighting. Not only is it simpler to write code while using Java, but it's also quicker. When it comes to the development of back-end apps, Java is the go-to solution for 90 percent of the organizations that make up the Fortune 500. Java serves as the basis for the Android operating system. Java is also essential for cloud computing systems like Amazon Web Services and Microsoft's Windows Azure and plays a key role in data processing for Apache Hadoop. Java Can Run on a Wide Variety of Platforms Java is a platform-independent programming language because the Java source code has been broken down to byte code by the Java compiler. This byte code may then be run on any platform by utilizing the Java Virtual Machine. Because it can operate on a variety of platforms, Java is sometimes referred to as a WORA language, which stands for "write once, run anywhere." Additionally, due to the platform-independent nature of Java, the majority of Java programs are developed in a Windows environment, even if they are ultimately deployed and operate on UNIX platforms. Conclusion To summarize, Java is a reliable and extensively used programming language that is important for the process of developing a broad variety of software applications. Possessing the appropriate resources can make a substantial difference in a beginner's ability to effectively acquire the language. This article features an overview of the five Java books that are considered to be the best for beginners and come highly recommended by Java professionals and industry experts. Because they include information on fundamental aspects of programming as well as object-oriented programming, data structures, and algorithms, these books are a good place for beginners to begin their studies. Beginners may get a strong foundation in Java programming and enhance their abilities in developing complicated software programs by following the directions and examples that are offered in these books and using them as a guide.
As an experienced full-stack developer with 30 years of experience, I’ve witnessed a lot of changes in tech, from the advent of the internet to the excitement of Java to the rise of cloud computing. Recently, I decided to embark on a new adventure: diving into Ethereum and web3. I was initially skeptical about web3/blockchain, but as I've delved deeper past the FUD and into the current state of Web3, I've come to appreciate its potential. And as the markets swing wildly on their views of the web3 ecosystem, there are a surprising number of projects and companies that just keep on building serious, and groundbreaking, components of the web3 stack. In this article, I want to explore the web3 developer stack, the pieces of that stack I like the best, and most importantly how you can use the web3 stack as a starting point to become a web3/blockchain developer. The Web3 Stack First, let’s look at the layout of the stack and the general categories. From the Infura Guide to the Web3 Stack Disclaimer: I’m cheating a little and using the logical breakdown used in the Infura Guide to the Web3 Stack to help organize my thoughts. It’s new (meaning it’s up to date, which is always important; web3 moves fast), well organized, and will help us get started a lot faster than trying to define the layout of the stack ourselves! I’ll use this layout to divide the stack into its main components: layer one blockchains, smart contracts, languages, front-end libraries, node providers, APIs, dev environments, testing, oracle, identity, and more. So, using that framework, let’s dive in! What Skills Are Needed To Be a Web3 Developer? First, what skills exactly do you need to be a web3 developer, and how are they different from what you already know? Well, to start, the good news is you need all those usual skills you’ve already learned as a full-stack dev (testing, DevOps, code reviews, etc) … plus a few new ones. Here, we’ll cover the ones you need to get started: Understanding and using layer one blockchains Smart contracts Smart contract languages Front-end libraries Node providers Dev environments Testing Identity Some of that probably looks familiar, while some of it is new. And you need to understand those new concepts pretty well to get started. For me, as a long-time Java dev, there were a few key differences between web2 and web3 that took a little time to sink in. Some of the mental shifts for blockchain technology included: Language Differences: Solidity, the main language for creating smart contracts on the Ethereum blockchain, has its own unique syntax and conventions that differ significantly from Java, such as the way Solidity handles data types, function visibility, and storage. Decentralization: Moving from a centralized, server-based mindset to a decentralized, peer-to-peer approach required a substantial mental shift. I had to learn how decentralized systems function, including concepts like consensus algorithms, cryptographic hashing, and distributed storage. Understanding these concepts was challenging but crucial. Gas Costs: In Ethereum, transactions and smart contract executions consume "gas," a measure of computational resources. I had to learn how to optimize my smart contracts for gas efficiency, which involved understanding the Ethereum Virtual Machine (EVM) and learning best practices for reducing gas consumption. Security: Writing secure smart contracts is critical in web3, as vulnerabilities can have severe consequences, such as loss of funds or compromised functionality. While security is always essential, the stakes are elevated in web3. I had to learn common smart contract vulnerabilities and adopt more rigorous security practices. Asynchronous Interactions: In the web3 ecosystem, interactions with the blockchain are inherently asynchronous, as they depend on network confirmations and block mining times. I had to keep this in mind when handling application state and user interactions. If you are confused by any of these, take the time to learn about them now. Then come back and continue as we look in more detail at each of the main categories of the web3 stack. Layer One Blockchains First, at the base layer is the layer one blockchain. These are, to me, the “primitives” of web3—they are the execution layer of the web3 stack. What you build and how you build it all depends on what layer one blockchain you choose. Some of the most popular include Bitcoin, Ethereum, Solana, Flow, and Polygon. Each one offers its own quirks, advantages, ecosystems, user communities, etc. There can be quite the (not always friendly) competition between these chains as they fight to see who can gain the greatest user and developer share. Most of the chains have either a for-profit company or a foundation behind them pushing the tech (and narrative) forward. As a developer, you don’t necessarily have to choose a single chain—some devs work on more than one at a time. Many of the concepts (decentralization, smart contracts) are the same among the chains but are just implemented differently. So, similar to learning one programming language to start, then using that experience to branch out into multiple web2 ecosystems, you can do the same here. I started with Ethereum, the most popular (and IMO easy to learn) chain. Ethereum has a huge community, long history, and tons of tutorials, examples, user groups, and resources out there to help you get started. Smart Contracts A smart contract is a self-executing program that runs on one of those above blockchains, like Ethereum. They are the code of web3 and where all the action happens. A smart contract is essentially a digital agreement between two or more parties, where the terms and conditions of the agreement are directly written into lines of code. The code and the agreements contained within it are transparent, tamper-proof, and automatically enforced by the network, without the need for intermediaries. In simpler terms, you can think of a smart contract as a digital vending machine. When you put money into a vending machine and select an item, the machine automatically checks if you've provided enough money, and if so, it releases the item you chose. Similarly, a smart contract checks if certain conditions are met (like receiving a specific amount of cryptocurrency), and if so, it automatically performs the agreed-upon actions (such as transferring a digital asset to the sender). Because the contract lives as immutable code, the outcome is publicly known. Most layer-one blockchains have smart contracts, each with its own variety of features and restrictions. Learning to code a smart contract is a necessity for web3 development. It’s where all the work is done. Here’s an example of an Ethereum smart contract. Languages To write those smart contracts, you’ll need to learn the smart contract programming language of that particular chain. Smart contract development is the meat of being a web3 developer, and where you’ll spend the bulk of your time learning. Each language, of course, is a little different and varies in the tooling, support, ease of use, and functionality. Solidity, the smart contract language on Ethereum, is the most popular. Plain Text pragma solidity ^0.8.13; contract HelloWorld { function sayHello() public pure returns (string memory) { return "Hello World!"; } } A Solidity “HelloWorld!” smart contract There are a lot of smart contract languages. Some are based on Rust, some on JavaScript, some on Python, and some are entirely new paradigms. Here are a few examples: Smart Contract Programming Languages (Source) Front-End Libraries The smart contracts running on the blockchains can be thought of as the back-end code of web3. Similar to web2 web development, in web3 applications we have front-end code as well. Instead of apps, we call them dApps (decentralized applications). Fortunately, most dApps are written in the same stack as web2! HTML, CSS, React, etc. To connect to the back end, though, we have to go through a node (more on that in the next section). And we typically have to connect using a JavaScript library, such as ethers.js and web3.js, which provide much of the logic we need for signing transactions, getting account info, managing wallets, etc. Typical web3 front end (source) Node Providers To access a blockchain, you need to go through a node. A node, in general, is a computer that is connected to the blockchain and participates in its network. It might be a miner or provide the computing power of the network. It might store and execute code or validate new blocks, etc. Whatever it does, you need it! They provide access to the blockchain data. So to read or write from the chain, you’ll need to connect to a node. Most people don’t run their own nodes. Rather, they use a provider that offers them an API, dedicated endpoints, scalability, reliability, etc. Think of node providers as IaaS, or a cloud provider for blockchain. For example, Infura offers access/nodes to Ethereum, Avalanche, Starknet, and others as both an SDK and an API. Other options include Alchemy and QuickNode. JavaScript const Web3 = require('web3'); const infuraURL = 'YOUR_INFURA_URL_HERE'; const web3 = new Web3(new Web3.providers.HttpProvider(infuraURL)); // Get current balance of address const balance = await web3.eth.getBalance(address); Using the Infura API to get a web3 address balance Dev Environments Just as in web2, web3 dev environments take care of the end-to-end workflow: editing code, testing, debugging, building, and packaging all in one tool or suite of developer tools. In web3, you get a web3 version of the extras, such as scripts for compiling and deploying smart contracts, dApp templates, and more. Dev environments also typically come with a local, dev instance of the blockchain. For example, I used the Truffle Suite to write, compile, and deploy my first smart contracts, which includes Ganache to create a local blockchain and Drizzle to create a front-end dApp interface. Inspecting RPC requests with the Truffle Dashboard Other options include Remix (an IDE), ChainIDE, Anchor (for Solana), Hardhat, and lots of others. Web3 dev environments have come a long way in a short time, and there’s probably something out there for whatever you need. Testing Testing is crucial in web3 because smart contracts are immutable, meaning they cannot be easily updated or modified once deployed. So you have to make sure they do what you want, and in a secure and reliable way, before you deploy. The good news is: testing is testing! So you’re probably already familiar with unit testing, automated testing, fuzz testing, and other testing options. You do a lot of the same things, just focused on web3. “Move fast and break things” is discouraged in web3, since millions (or more) of dollars can be on the line. A lot of the dev environments mentioned above provide a testing framework. But you’ll probably want some specialized tools as well to do things like property-based/fuzz testing. You might also look into third-party audits of your code. If large amounts of money are involved in your smart contract, you’ll want a professional, second pair of eyes to check everything. Identity IAM (Identity and Access Management) in web3 refers to decentralized methods for managing your identity, authentication, and authorization. It lets you own, control, and share your identities and data without relying on any centralized authorities. Identity in web3 means self-sovereignty, user control, and privacy. So when you are interacting with a dApp or building a dApp, you most likely need to integrate with one of the web3 wallets, such as MetaMask. MetaMask is a browser extension that is an Ethereum wallet. It allows users to interact with dapps, prove who they are, and manage their assets. Other popular wallets include Temple (for Tezos) and Argent (for Starknet). But there are lots of choices out there. MetaMask Let’s Start Building! There are a lot of other areas in the stack, and this frankly has just covered the fundamentals of web3, but these are most of the key areas and a great road map to get you started! Good luck as you venture into web3. Don’t get taken in by the FUD: focus on the groundbreaking projects and parts of the tech stack. Make sure to take your time to learn the new concepts, and you’ll be well on your way. Whether it's NFTs, defi, DAOs, metaverse, music, film, or something else, web3 was meant to empower users, redefine industries through decentralization, and create a more open and equitable world. It still has a way to go and is having some bumps as it grows, but it’ll get there. Enjoy!
It's time to make noise. Developers need to take back their happiness and their productivity. But where should they start? On this week's episode of Dev Interrupted, we're joined by Justin Reock, Field CTO and Chief Evangelist at Gradle. With a mission to mitigate the toil, friction, and frustration felt by so many developers, Justin has become a tireless advocate of Developer Productivity Engineering. Listen as Justin explains DPE by exploring the connections between productivity, developer experience, and joy. Episode Highlights (3:50) What is Developer Productivity Engineering? (8:14) Creative vs. destructive flow (16:21) Metrics and visibility (22:37) Open-source supply chain security (24:01) Blockchain, AR, and VR Episode Excerpts How Facebook, Netflix, and LinkedIn handle developer productivity engineering Justin: That's one of the things that actually differentiate DPE from DevOps a little bit is that there's the human factor, right? Like the platonic ideal of a DevOps team is supposed to be not a team at all, right? 100% automation, no humans in the loop, right? But with developer productivity, you have to have humans cascading this practice, caring about, you know, the metrics that they see on the dashboard and then correlating that to developer experience. Without that empathy, then you don't get the focus; you don't get anything actionable that really changes the culture of the business. So that is a differentiator is that you need those people to be thinking about these problems. But then you know that that ends up, and ideally, these people have written code, right? I mean, right? It's very special you come across these developers who, like, truly care more about the performance of their team. And what the whole team is, is going through in terms of the developer experience versus their own experience writing code, and they're everywhere. They're in every organization. I mean, that's the thing is, like, a lot of folks have already been doing DPE. They just haven't been calling it that, you know, or maybe it's a part-time, couple hours a week that some development leads spends on productivity. But what we have to see now is a shift in focus to making this a center of excellence. The companies that are really doing this, right, you know, the Facebooks, the LinkedIns, the Netflix, you know, they have dedicated teams of hundreds, productivity engineers, it's 1,000s of developers. And that's the only way, I think, to kind of keep enough of a focus in the industry to allow this to be a transformative change as opposed to just minor tweaks. Do CEOs Care About Developer Experience? Justin: I'm a big fan of the whole Malcolm Gladwell 10,000 hours for expertise type of thing, but is it 10,000 hours waiting on a bill to complete, or is it 10,000 hours writing code? So obviously, if you're able to spend more time on that part of your learning and your experience, that only makes sense that you'd be able to onboard faster. You won't run screaming from the industry because you're like, oh, this is not what I signed up for. This sucks. So, I think that I mean, there's all these impacts, and the time is right for it now. I mean, Gartner released a CEO report a couple of months ago, the 2022 CEO report, and it was like, I don't remember the exact statistic, but it's like a 40% increase in terms of CEOs that are now... That they actually have Developer Experience on their radar, which is very good, but also very acute, right? I mean, just a couple of years ago, it was way low on the list of CEO concerns, and now it's one of the top concerns. Engineering Insights Before Anyone Else... The Weekly Interruption is a newsletter designed for engineering leaders by engineering leaders. We get it. You're busy. So are we. That's why our newsletter is light, informative, and oftentimes irreverent. No BS or fluff. Each week we deliver actionable advice to help make you - whether you're a CTO, VP of Engineering, team lead, or IC — a better leader. Get interrupted.
Gene Kim
Author, Researcher, Speaker, Director, DevOps Enthusiast,
IT Revolution
Miguel Garcia
VP of Engineering,
Nextail Labs
Jade Rubick
Engineering advisor,
Jade Rubick Consulting LLC
Dan Lines
COO,
LinearB