How do you increase programming productivity?

One thing we've found very useful was Technical Sprint Planning. After the PO presents the new stories in the Sprint Planning, the dev team gathers again later that day to discuss the best solution(s) to implement their tasks. The team is more committed to the solution, time wasted in code reviews is optimized and, at the end of the US implementation, the solution in less prone to misalignment between teams.

You cannot know what you want to achieve if you don't understand why you're being asked to write code in the first place. The answer might be a one line hack that works in 100% of cases for the next 3 years. There is no way to be more productive than avoiding doing unnecessary work to accomplish the actual task in front of you.

Before Starting the code, definitely define what you want to achieve. Break down complex task into smaller, manageable goals. Having clear plan helps you stay focused and organized. For instance, if you are building a website, define specific goals like creating a homepage layout or implementation of user registration system. This clarity helps you structure your work effectively

From my experience, Before you jump start to working, 1. Define the Project Scope and Goals : visualize what you want to accomplish with the task. review with peers. 2. Identify Subtasks: Break down the Tasks into manageable steps. -> Create a list of all tasks required for completion. Divide large, complex tasks into smaller, more achievable subtasks. 3. Prioritize Subtasks: -> Determine the order in which tasks should be completed. -> Consider dependencies between subtasks (which tasks rely on others being finished first). Identify critical subtasks that are essential for task progression. 4. Estimate Time and Resources: Allocate realistic time frames for each subtask. 5. Then execution of task will be smooth.

Planning your tasks means writing down what you need to know then reading about how to do those tasks and determining who you'll reach out to learn more about your task. Creating an outline and plan on how to learn to do the task will always be more productive than diving head first into a task and trying to figure it out on your own. Otherwise, we make the mistake of mindlessly trying things to solve the task without a strategy on next steps.

The WBS- Work Breakdown Structure will be he most critical aspect of planning and it shall be influenced by Low Level Design Practices. The WBS will layout out the tasks and its important to know how granular they shall be. If they are too granular, it will result in wasted time for managing & governing. If they are too high level, they will be bloated. The task breakdown be determined by the unit of Work which are unit & QA team testable and reviewable. The thumb rules could be - don't' create a task if its going to take less than 4 hours rather include in other tasks, avoid inclination to split based on # of programmers /developers rather try to keep the sanity of task/s intact and have peer programming.

To increase programming productivity: 1. Master Your Tools : Learn IDE shortcuts and features; use productivity-enhancing extensions. 2. Efficient Coding Practices: Use Git effectively, conduct regular code reviews, and write automated tests. 3. Time Management : Apply the Pomodoro Technique and prioritize tasks using tools like Trello or Asana. 4. Continuous Learning: Stay updated with tech blogs, attend webinars, take online courses, and engage in peer learning through coding communities. Implementing these strategies can significantly boost your programming productivity.

From a software engineer stand point, learning how to abstract is the key for building maintainable software, try to implement your task in pure functions first, if cannot, try to minimize the side effects and use object/local scopes to enclose all side-effects. Always make sure your code is self-contained and plug'n'play for everyone else, sometimes even with slight performance trade-offs.

Plan your tasks by setting clear goals, breaking them into smaller steps, prioritizing what's important, and assigning realistic timeframes. Use tools like project management software or methods like Scrum to help organize and execute your plans effectively.

There is one tool guaranteed to increase developer productivity that everyone forgets about. It doesn't matter if you have the best IDE, libraries, and code assistants if you are developing on a slow computer with a small screen. At my previous company, we bought developers the latest and greatest equipment and saw a measurable boost in productivity and morale.

Certainly! Choosing the right tools is crucial for improving programming productivity. Select a Suitable Code Editor or IDE: The choice between a lightweight code editor (e.g., Visual Studio Code, Sublime Text) and a full-fledged integrated development environment (IDE) (e.g., IntelliJ IDEA, Visual Studio) depends on your needs. Code editors are often faster and more customizable, while IDEs offer more comprehensive features like debugging, project management, and code refactoring. Ultimately, the right tools depend on your specific programming language, project requirements, and personal preferences. Regularly evaluate and adapt your toolset to stay productive and up-to-date in the ever-evolving field of programming.

Offer the paid version of ChatGPT to developers and watch their development speed triple. An example, with just a database table structure image upload, ChatGPT will generate the necessary code for models, controllers, services, and repositories. All the developer needs to do is copy, paste, and make minor adjustments—typically less than 5%.

Choosing the right tool with the research plays a vital role. Be it IDE or programming language, Version control tool and server details as well. Make sure you decide before coding.

AI, AI, AI... if there is any tool that can really increase productivity, then that is more clear when talking about AI. 1. IDE AI plugins will save you a lot of time through advanced auto-completion, auto-corrections, code suggestions, docstring generation, and more. 2. Many coders jokingly say that their main job is Googling stuff. Not anymore, now you can get a lot of the information you would have spent hours searching for on Google or reading through the documentation, just by asking AI tools like ChatGPT. And if you have trust issues when using AI like this, just ask for the source.

I have heard great things about nine tails whips. Once your developers taste the sweet kiss of the leather, they will do anything you ask them to in order to avoid it or get more of it. As long as you're fine with your new role as a cenobite, productivity is guaranteed to go up.

Conhecer a fundo a ferramenta que usa é fundamental para a produtividade. Saber todos os recursos e configurações disponíveis vai te ajudar a programar melhor e mais rápido, assim como conhecer todos os recursos de depuração de código.

On top of personal productivity tools, Communities of Practice can be a powerful factor in e.g. sharing best practices and a place to turn for advice / tips. They don't work automatically though; it requires effort - but when paired with a company culture promoting cooperation and supporting sticking your head out if you need a hand, CoP's can really shine.

In the new world of generative AI, there are many relevant tools that can help speed up programming efficiency. AI tools like autocompletion (e.g., TabNine), code analysis (e.g., CodeClimate), and bug detection (e.g., Diffblue) enhance programming efficiency. They save time, improve code quality, and simplify tasks like code review, testing, and documentation generation. Code translation tools (e.g., GPT-3) help with language conversion, while code refactoring tools enhance code structure. AI is revolutionizing software development, making it more accessible and efficient for developers.

It is important to select tools that form a coherent and interlocking ecosystem and that are accessible to all teams members, especially tools targetting architecture, modelling and design.

NASA has a fantastic set of software development guidelines. One of their goals is to ensure that their software is easily readable and decipherable. If you read those rules, you'll see that they achieve this by significantly restricting the kind of code they accept. Accordingly, their documentation doesn't need to be very extensive, because they only accept code that is written in a way that's easy to understand. You need to have a clear goal in mind when you set out your coding standards. Then you need to be ruthless about rejecting any and all code that doesn't meet those standards. That's how you pull off moon-shots.

This is very true, being able to come back to your codebase and understand what the code is doing is so crucial, not only for you but for the next person that will work on the same project. I know this goes unmentioned but truth be told one of the hardest task in software is naming convention. I have learned though that with great documentation and adding an UML( Unified Modeling Language) diagram helps alot with code maintenance. So I highly recommend, always visualizing that UML for any big feature and documenting important part.

Code is read more than it's written, quite clear. So make it worth reading, hold that dear. Don't be too clever, keep that in sight. Or reading ye code might be a plight.

Using established coding standards makes it easier to read code from other developers - and this is important because you'll often be modifying or building on top of something existing. Establishing coding standards is, of course, best done when starting a greenfield project. Considering there may be 'local variations' across teams it can be beneficial to have corporate standards that the individual teams can adjust to their taste. If you are not on a greenfield project, which is often the case, coding standards can still be applied but takes more work: one approach is to suppress violations in all files to begin with. Every time you then have to make changes to an existing file you remove the suppression and fix all violations.

Plan to search through your existing team's code repositories to learn and reference existing standards. A great way to save time and not "reinvent the wheel" is to utilize the advanced search capabilities in Azure DevOps to find similar code solutions already implemented, copy, and paste them into your own, and customize!

Coding standards are often undervalued, yet they significantly streamline the development process. It improves: - Consistency - Readability - Collaboration - Maintenance - Troubleshooting - Testing For instance, for some languages distinguishing between a variable, a function call with no parameters, or a specific structure is not straightforward. Following a coding standard saves us a lot of time and effort.

When we talk about coding standards, we have to pay close attention to the meaning of the words "code quality standards." Nomenclature and conventions should help your team develop better and more maintainable software. It is extremely important that a code pattern be suitable for your software, and not the other way around, with some exceptions, of course. Your software should not be held hostage to code patterns, but should use them to make the lives of your engineering team and its users easier.

Making sure that your tooling supports the standards is also key - setting up VSCode to run your linters and tests whilst you work on a feature is a game changer and saves so many headaches down the road.

Writting software is kind of an art. You want it to be readable, concise, bug-proof, non-repeating and clean. Make sure it takes another developer less than three seconds to understand each line you have written.

Coding standards are super important because they make your code look clean and easy to understand. They cover things like how you name your variables, how you indent your code, and how you handle errors. Sticking to these rules can help you avoid mistakes and make your code simpler and easier to work on with others. It’s a good idea to follow the standards that are popular for your language or project. There are also cool tools like linters and code reviewers that can help you keep your code up to these standards.

We should not think about creating production code without the respective testing code. I strongly advocate using Test Driven Development to create testable and tested code from the beginning, but having unit tests may not be enough. When dealing with distributed computing in the form of microservices deployed to public cloud, it is importante also test how different components work together. Integration tests are the answer to validate it. The cherry on top is contract testing. By making sure your changes keep API intact, alongside with the other above mentioned tests layers, your services will be unbreakable by changes, thus allowing high degree of confidence when it comes to continously deploying those changes into production.

Make it fail. It's disappointing to see your code fail, but the earlier you can get it to fail, the more time you'll have to fix it and to work through the consequences of a fix and any other ramifications of what you've discovered.

Some tips to make your testing more effective and efficient: - Decouple your code: Coupled code is hard to test, maintain, and reuse. It also leads to more test failures when you change one part of the code. To avoid this, follow the principles of decoupling. - Pass only what your functions need: If your function only needs a specific attribute of an object, pass that attribute instead of the whole object. - Test your business logic: Your business logic is the core of your application, and it should be well-tested. Try to write functions that encapsulate your business logic, and test them. - Have some integration tests: Integration tests check the interaction between different parts of your system, such as services, databases, or APIs.

If it doesn't have to work, we can build _anything_ infinitely fast. What does "working" mean in this tasks context? Are we building something with the cheap guarantee of "works on my machine, once last August" or "continues working in production and enables the next programmer to also make changes without accidentally undoing anything critical"? Defining what "working" means is part of identifying the tasks, and over time can be abstracted to team conventions or heuristics.

Perfection over a period of time is acceptable. If you try to be perfect, possibility of failure is high. So code dirty so you have the skeleton, make it right then share for review.

As a tester, we have to make sure you understand the different scenarios through which this unit of code is going to get executed. Understanding the purpose / functionality is very important. Usually I look at how to make sure every line of code what you write is getting executed by creating different test scenarios. Creation of test data is one other important thing which folks miss according to me. They test and approve the code based on the DATA they have rather than creating data for each scenario. Creation of data for exceptions is the key to reduce the incidents in the production. Automation of test cases is as good as DATA what we have and the scenarios you build to test.

I found AI tools very helpful in writing test cases. Especially if you know your job well, AI acts as an assistant speeding you up. Copilot and other ai tools have been improving gradually. As of today GPT's are writing good tests for python, struggling a bit with Java and reactive programming. With the current speed we should expect AI tools to improve and be able to help reduce 40-50% of testing efforts by the year end.

A abordagem do Test Driven Development (TDD) é, na minha visão, uma prática essencial para criar um código não apenas testável, mas também robusto desde o início.

Automated testing is crucial for enhancing productivity, preventing rework, and delivering superior quality. The more tests your codebase contains, the fewer manual tests your team will need to conduct after each new commit. This reduces the challenges associated with debugging, allowing the team to invest more time in developing new features. Some of the most important types of tests: 1. Unit Tests: These are low-level tests that focus on individual components or functions in isolation. 2. Integration Tests: These tests concentrate on the interactions between different components or systems, ensuring that multiple code units function well together. 3. End-to-End Tests: These tests simulate real user scenarios.

Refactoring is great to make a code base more pleasant to work with. However be careful of refactoring to feel productive and keep yourself busy. The value of code is the business logic it provides. If it's well tested and does what it's supposed to do, it likely doesn't need a huge refactor. Another perspective to have is the return on investment of refactoring. If it takes days to refactor files that are touched once a year, that time can be better leveraged on other activities.

One of the best ways to refactor a monolithic code base is to use the Strangler Fit pattern. Discover the project and identify a module or service that is highly cohesion and can be loosely coupled in the integration. Extract that into an independently deployable service while keeping the old implementation still. Use a load balancer at the API level to gradually direct more traffic from the monolith to the new microservice. This helps to limit the blast radius of any potential issues and reduce the business risk. To understand which portion of the app should be refactored out, run Event Storming with the team and apply non functional requirements to identify challenges and risks.

Over time, as software evolves and requirements shift, code can become cluttered or inefficient. This is where refactoring steps in, ensuring that the code remains clean, efficient, and maintainable. It involves revisiting existing code, identifying redundancies, and implementing more efficient patterns. By doing so, developers can enhance code readability, reduce complexity, and make future modifications more manageable. Refactoring is not merely a technical exercise but a commitment to quality, indicating a developer's dedication to craftsmanship, adaptability, and the pursuit of excellence in their work. It's a proactive approach, ensuring the codebase remains agile and robust amidst the ever-changing landscape of software development.

In my experience, to do the productive refactoring, the software developer should be familiar with different programming paradigms and languages, even if they are NOT directly applicable. For example, it won't hurt software developer refactoring Java codebase be familiar with functional programming (think Lisp or Standard ML -like languages), logical programming (Prolog), stack programming (Forth), even though these things will not be applicable (well, maybe some concepts of functional programming will be).

One mantra I follow when working on updating any existing code - "Always leave the code cleaner than you found it." (the Boy Scout Rule)

Everyone evolves and grows, also your experience and knowledge in a programming language, what you developed with 3 months of experience will not be the same as what you would have done with 1 year of experience. So if it allows you, refactoring your code is very valid. Then when we inherit developments from other people or companies, we will apply this refactoring also to improve performance or to scale the functionality. If we encounter obsolescence of some functionality or some technical issues we will also apply refactoring.

One thing I've found useful is to write code expecting the requirements to change and evolve over time. The idea is that the code written needs to be built with components to make it nimble enough so that as requirements come in they don't cause you to change everything everywhere all the time. This is the quiet part that won't be said out loud. This is an important aspect of the art of software engineering and the tug of war that happens between an evolving business and and complex codebase. Every day without direction you need to ask yourself, how can I make this better than it was the day before?

Refactoring is essential for maintaining clean, efficient code. It's about restructuring and cleaning up your code without altering its functionality. This process reduces complexity and improves readability, making your code easier to manage and extend. Regular, incremental refactoring is key. It keeps your codebase manageable and prevents it from becoming unwieldy. Adhering to clean code principles helps streamline this process. Remember, refactoring is less about immediate gains and more about long-term code health and maintainability. So do it carefully and in small chunks.

Find a way to incorporate refactoring into your standard workflow. Convincing business leaders of the value of a refactor is always an uphill battle, but consistently maintaining high quality code over time with small, targeted refactors prevents the need for large, costly refactors.

Tools like Intellij idea would help identify duplicate code detection. However using alternate plugins and customizations that can help identify duplicate code would help save time to reduce, reuse and not only enhance the coding capabilities but also to improve productive coding

Read tutorials or watch videos of different coders explaining the same topic. Each coder might explain the same topic in a different way, offering you a fresh perspective or a deeper understanding of a topic you thought you knew well. You might also gain insight into a small, but important detail that you had previously overlooked.

I think this is the most important aspect that is never in the limelight when you talk about productivity. Writing code is the ultimate execution step in software engineering, before you write code there is a lot planning, architecture, and many more steps. Learning helps you broaden the number of ways you can solve problem, thus you become a problem solver rather than just a coder.

I continually acquire new skills that align with my goals. Exploring tutorials and diverse sources, I gain fresh perspectives and deepen my understanding. For example, watching different coders explain a topic sheds light on nuances and enhances my knowledge. I believe in continuous self-education, embracing each experience—faults and successes—as a valuable lesson for ongoing professional growth.

Self-education is one of the key factors to professional growth. Stay hungry, stay unsatisfied by your abilities - keep in mind that after you have grown even a little, your currently best achievement will look more natural and mundane. Each fault, each success can and should be turned into a lesson and thus a stepping stone to further growth.

Uma coisa que faltou ser ressaltada é que aprender coisas novas não necessariamente quer dizer aprender as tecnologias do momento, pelo contrário, quer dizer aprender como as bases e fundamentos construíram tudo que interagimos.

Programming is always, in a way, a creative process. Like a painter, we look at our "creation" up close or step back to watch it as a whole. When we press run, we watch it get animated and hope to see its well-ordered movement. Like an illustration or piece of music, code is always a work in progress, never really finished, more "good enough. " But contrary to a painting, code can be an iterative process. You don't show the progress of a painting; we do. So, the most essential skill to work on is knowing when to stop working on any feature.

Before getting on a new development project I would recommend to refresh your memory and check out some books like Code Complete II from Microsoft and Pragmatic Programmer series. Make sure you choose the correct path for your project.

Conhecer várias linguagens e tecnologias é essencial para desenvolver um bom sistema. Não se limite apenas ao desenvolvimento, estudar servidores, redes e serviços como o HTTP é essencial para produzir com qualidade.

Learning new skills is essential for programmers. Here are some suggestions that might help: 1. Coding challenges. Apply your skills in real or simulated projects. Sites like LeetCode or HackerRank offer coding challenges in different languages & domains. These can help you improve your logic, syntax, and design skills, as well as test your knowledge & understanding. 2. Find a community. Seek feedback & support from others. Platforms like Stack Overflow, GitHub, or LinkedIn can help you connect with other programmers, ask questions, share projects, or get feedback. Also, find a mentor that can motivate you & keep you accountable for your learning goals. Learning new skills is a lifelong journey, but can also be fun & rewarding.

Stay away from Distractions! In today's digital age, constant screen time and social media notifications have conditioned many, especially the younger generation, to have fragmented attention spans. This is concerning, especially for fields like programming, which demands deep focus and uninterrupted concentration. To truly excel in coding, it's important to develop a habit of disconnecting. Turn off those notifications, set aside devices, and immerse yourself in problem-solving. Often, when we step away and give our minds a break—be it during sleep or a shower—we find solutions to the most perplexing issues. This isn't magic; it's the brain's way of processing when free from digital distractions.

Programming productivity is all about feedback loops. Feedback loops are how programmers "see" where they are going. If you have fast, reliable, and low ceremony feedback loops then programmers can quickly iterate. If you don't have fast, reliable, and low ceremony feedback loops then programmers cannot quickly iterate and will be programming based on assumptions and luck. If you have fast, reliable, and low ceremony feedback loops then it's possible for programmers to quickly iterate although other constraints will be revealed depending on the problem at hand. Fast: less than a millisecond ideally. Reliable: always giving a consistent result. Low ceremony: easy to use e.g. on file edit/save.

Limit the number of meetings. Non-developers (even managers who code) have a tendency to underestimate the impact of meeting. But a 30 minutes meeting also means: - 30 minutes before the meeting where you cannot start anything else, because if you do, you're going to be late for meeting or won't be able to concentrate; - at least 15 minutes after the meeting to get back into the zone; - all that assuming that everybody arrives on time, that the meeting is productive and finishes on time. That's an additional lunch break in the middle of the afternoon, except instead of being relaxing it's actually stressful for the developer.

Sometimes, when you feel blocked or can't figure out what to do with a task, pair programming or even gang programming helps a lot .

In the team, we recently turned Tuesdays and Thursdays into no-meeting days for developers, and we can see an improvement in completing projects on time.

Automate. Automate. Automate. If you find yourself typing a series of commands to complete a mundane task, put those commands in a script. This way you only need to remember and type one command. This will free up your brain and time for other more important things. If you solved a problem manually more than once, that is too many times. Figure out a way to automate the task and share the solution with the team.

Continuously and mercilessly reduce your Work in Progress. Stop depending on inspection after the fact and focus on building quality code that can be pushed to production as soon as possible. Substitute Pull/Merge Requests by Pair Programming or Mob Programming. Inspect your flow to remove blockers and dependencies. All these elements generate waste in the form of waits and rework. Aim for a single-item flow.

While programming isn't as demanding as say, being a general contractor, it still can take a lot out of you. Your mind can get overworked. Take breaks to keep yourself fresh. Many times in my career I run into a mental road block that is cleared just by stepping away from the problem for 15 minutes.