How can you develop and deploy code in IT?

Choosing a language is highly dependent on the functionality of the application you are building. Python and PHP are more free form and allow you to do whatever you want, Java and C# are more structured and highly performant. Keep in mind though that most large code bases actually will wind up with a conglomeration of multiple back end languages because certain languages perform certain tasks better than others (there is a reason Python is used a ton with AI and ML systems). Don't be afraid to utilize different languages to get around the limitations of the language you originally picked. Also, most languages nowadays have more ability to communicate and play nicely with other languages and common communication mediums to use like JSON.

What a horrible selection of deeply flawed languages! ?? Python gives whitespace meaning. PHP is u$g$l$y with OOP bodged in as an afterthought & JavaScript untyped. K&R & Stroustrup are turning in their graves. What have we done! ??

I would definitely consider below mentioned points 1. Project Requirements: Determine the necessary functionalities and scalability for the project. 2. Community Backing: Evaluate robust communities, comprehensive documentation, and available resources. 3. Team Expertise: Assess the team's skills and the learning curve associated with the language. 4. Environment: Explore libraries, frameworks, and tools that match the project requirements. 5. Long-Term Maintenance: Consider the language's endurance and alignment with industry developments.

With Typescript (and javascript) you can do code with performance for a wide set of use cases from web (both front with angular and back with node) to embedded. Even some industrial controllers believe it or not. That said it lacks presence on ML and AI matters. Not a Panacea.

The "right" language for a project depends strongly on the skill base of the group that has to develop and maintain it, and also on well the language's ecosystem matches the project's needs. That said, languages have improved over time; new projects in languages introduced over twenty years ago are becoming less common if not rare. If you have the luxury of choice, consider e.g. Go rather than Ruby, Python, C/C++, or Java, and Rust rather than C/C++, to take advantage of their modern design. Not only are they easier to write correct programs in, they are beautifully designed and just plain easy to use compared to older tools.

Generally standards for most basic functions need to be abstracted by using abstraction and inheritance techniques. This will surely make code testing , versioning and maintenance easier and result in a more compact and clean code base Also using sub projects for such layers will help in protecting proprietary logic from those who are just permitted to use those classes / objects and further helps in training coders to adopt to such templates very easy Identification of such framework elements which are needed throughout an Application will help us reuse code base with multiple projects , thereby reducing development time overall Adopting template driven architecture also allows us to migrate code bases between programming languages easy

To save time it is important to standardize the development environment across the team. This includes same IDEs, plug-ins, tools, extensions for every developer. This significantly reduces the over head of getting new team members up to speed and also reduces the risk of odd environment specific bugs in your code or infrastructure.

A well-structured development environment is the bedrock of successful software development. Choosing the right text editor or IDE is pivotal. Selecting the appropriate compiler or interpreter depends on your programming language. Version control is a must for tracking code changes, collaborating effectively, and maintaining a history of revisions. Git, along with platforms like GitHub and GitLab, empowers teams to work cohesively. Quality assurance is paramount. Employ testing frameworks like JUnit (Java), pytest (Python), or Jasmine (JavaScript) to automate testing. Debugging is an art. Utilize debugging tools integrated into your IDE or standalone options like GDB (GNU Debugger) to identify and rectify errors efficiently.

A productive dev environment requires the right tools. Non-negotiable: version control like Git for tracking changes and enabling experimentation. Leverage package managers to tap into open source, avoid reinventing. Automated testing frameworks are mandatory for quality and maintainability. Use Docker to encapsulate the environment for ease of setup and deployment. Cloud platforms provide infrastructure, collaboration, and scaling. Optimization tools help find bottlenecks as complexity increases. Track project activity with issues and wiki trackers. Choosing an IDE that fits your workflow is key. Invest time evaluating options. A robust environment with the right tools pays dividends in velocity, stability, and quality long-term.

Generally, Popular Source Code Management tools like git provide VCS in which code changes can be tracked. For Every integration adding a test case ensures that the System as a whole works fine without breaking other parts. Testing on Different Test cases and Loads can help us to improve the code and Synchronization.

I have found Test driven development as a practice to be particularly useful because in practice we write the test case first and then run the test which will obviously produce a failure result. Then we write just enough code to make the test pass. This would ensure that we don't write more code than needed which may bloat the codebase and also makes us work efficiently (write test and code at the same time rather than write the whole code and write bunch of tests to cover them). It also helps us keep the commits small. In case you do pair programming you can switch roles for every test case or one person writes tests and other person write the code to make it green.

One of the most crucial steps in the software development life cycle is testing. The history of software development has proven that not testing a product can be extremely costly, and even proper testing cannot guarantee the absence of all errors. Testing only verifies that a product passes scenarios within the scope of the testing strategy. Therefore, it's vital to formulate a comprehensive testing strategy and evaluate it in all possible scenarios your product could face. This ensures that your product is stable and can operate as expected under normal, edge, and error conditions.

The emphasis on writing clear, concise, and consistent code following best practices is commendable. However, it's worth highlighting the importance of Test-Driven Development (TDD) as a proactive approach to code quality. TDD involves writing tests before implementing the actual code, ensuring that each unit of code is validated against expected behavior. This not only enhances code quality but also contributes to a more resilient and maintainable codebase. Kent Beck's (who coined the term) book "Test-Driven Development: By Example" is an invaluable resource detailing the benefits of writing tests before code. In essence, adopting TDD enriches the development process, promoting robust code, and aligning with industry best practices.

Additionally, incorporating pair programming and Test-Driven Development (TDD) can significantly enhance the quality and reliability of your code. Pair programming involves two developers working together at one workstation, which promotes knowledge sharing, reduces errors, and improves code quality through continuous review. A great way to start pair programming is by having one developer write the tests while the other writes the code.

As a PHP developer using Laravel, I emphasize clear code and rigorous testing. Laravel's elegant syntax and tools like Eloquent ORM promote readability. Integration with PHPUnit enables diverse testing, from unit to browser simulations. Regular code reviews and continuous integration ensure quality and alignment with specifications. These practices, rooted in Laravel's robust framework, are vital for efficient, scalable, and maintainable code.

Building and deploying code is one of the particular things in the SDLC that, while it will change, once it is set up will have relatively little change. The best thing to do is to automate the process as much as you can and to select processes that appropriately support the application being deployed, if you have Docker containerization employed by the application you can use Kubernetes to deploy those containers to live environments and thereby save time in terms of setting up local development environments and manual deployment processes. If you deploy to a clould platform, choose the platform that best suits the application: AWS is a good default, Azure offers more customization and privacy, Google's data handling is second to none.

As I said before, the deployment is tied to CI/CD. If we want to have a good deployment in our environments, it is essential to be able to use ci/cd, which will allow us to do things in the best possible way.

Development Process: ------------------------ Requirements Analysis Design Coding Code Reviews Testing Debugging and Refactoring Documentation Deployment Process: ----------------------- Version Control Build Automation Deployment Environments Continuous Integration/Continuous Deployment (CI/CD) Containerization Orchestration Monitoring and Logging Rollback Plan Security Measures User Acceptance Testing (UAT) Post-Deployment Support Building and deploying code are critical steps in delivering software. They ensure that code changes are transformed into executable artifacts and safely introduced into different environments. Automation plays a key role in achieving efficiency, reliability, and consistency in these processes.

Follow naming conventions and language best practices. Build a robust automated test suite covering units, integration, functionality, and performance. Leverage test-driven development. Validate edge cases. Enable continuous integration. Strive for high test coverage percentage. Refactor periodically to maintain clean code. Clear requirements and tests protect against regressions. Use version control to experiment freely. Frequent testing gives confidence when changing code. Well-tested and high coverage code saves headaches long-term. Investing in comprehensive testing is time well spent.

Creating a CI/CD (Continuous Integration/Continuous Deployment) pipeline with GitHub Actions involves defining workflows in YAML files within your GitHub repository. GitHub Actions allows you to automate tasks, such as building, testing, and deploying your code. No milk is required when using Github 4 CD/CI

Never stop documenting. It is something that sometimes seems boring, uncomfortable and unnecessary, but for me it is extremely important to move forward with it so that tomorrow is not a problem. Be concise in how we are developing. Make it very clear what we are working on in git, organize it and make the versions of the published code clear. Always think that another person will be working on the project.

Well first of all, let's talk about code development: - understand the business requirements and design an architecture diagram for the solution - decide what language or tech stack you want to continue with - develop code and pass it through at least development integration testing Moving onto deployment: - Make sure you pass rigorous testing cycles like system integration testing, QA/QE, Performance testing. - All these tests ensure that your functional and non-functional requirements are met and your code is performing as required - Decide which CI/CD pipeline you want to deploy your code to (I usually prefer GitHub workflows CI/CD) - Decide deployment and rollback strategies - Once code is deployed, do a sanity test for live traffic

Building and deploying code extends beyond tools and practices; it's about fostering collaboration. Embracing a DevOps culture, where developers, testers, and operations work in unison, streamlines the process. Tools like CI/CD and containerization are vital, but their true value lies in integration within a cohesive team workflow. This synergy leads to efficient deployments and high-quality code.

Best Practices --------------- Code Review Before Deployment Testing in Production-Like Environment Collaboration and Communication Feedback Loop Regular Audits