Testing code before submitting it is a crucial skill for any software developer, especially if you want to impress your interviewer, avoid bugs, and maintain code quality. In this article, we will cover some of the best practices and tools for testing your code effectively and efficiently.
Depending on the language, platform, and project you are working on, you will need to choose a suitable testing framework that supports your needs and preferences. Some of the most popular testing frameworks are JUnit for Java, pytest for Python, RSpec for Ruby, and Jest for JavaScript. A good testing framework should provide features such as assertions, test runners, mocking, code coverage, and reporting.
As someone who mostly working on performance, one thing to add is add a performance test. With performance test, you can compare before/after to justify your changes. Also, you can use it to drive performance investigation in your new code, to catch low hanging fruits and avoid surprises. I normally check CPU, allocation, and memory.
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If you're writing .NET code, xUnit is a very popular testing framework but NUnit and MSTest are also great frameworks. Many developers also like to use the FluentAssertions NuGet package for very flexible and readable assertions.
A test case is a set of inputs, actions, and expected outputs that verify a specific functionality or requirement of your code. You should write test cases that are clear and concise, meaning that they have a descriptive name, a clear purpose, a well-defined scope, and a minimal amount of dependencies. You should also follow the AAA pattern: arrange, act, and assert. This means that you should first set up the test data and environment, then execute the code under test, and then check the results against the expected outcomes.
Most of the mistake that people usually make is design automation test case with manual testing mind set. They always want to add too many verification points in one test case. This will increase flaky chances, increase troubleshoot time, and lost forcus on their test objective. In my opinion, one test case should have one test objective only.
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Negative testing, specially covering rare scenarios would be a great approach to ascertain code is safe and ready for integration.
Whichever testing framework you use in your code, make sure to see how to best parameterize your tests. This will allow you to have one test method that tests many different test cases without duplicating the test many times.
Testing your code is not a one-size-fits-all process. You should use different types of testing to check different aspects of your code, such as functionality, performance, security, usability, and compatibility. Some of the common types of testing are unit testing, integration testing, system testing, acceptance testing, and regression testing. Each type of testing has its own advantages and disadvantages, and you should choose the appropriate ones based on your goals and resources.
From Android perspective, I suggest 3 kind of tests: 1. Unit tests: Tests business logic. 2. Scenario or Integration tests: Tests if a scenario is working end to end after optionally mocking the API response. 3. Layout tests: Checks if layout looks fine for different languages in different resolutions.
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If you're serving up an API to an external consumer like another service/product within your system or even a customer-facing API, you may want to consider adding in contract testing to ensure that you don't introduce any breaking changes to the consumers of your API.
Manual testing can be time-consuming, error-prone, and inconsistent. To save time and improve accuracy, you should automate your testing process as much as possible. This means that you should use tools and scripts that can run your tests automatically, without human intervention, and report the results and errors. You should also integrate your testing process with your version control and continuous integration systems, so that you can run your tests every time you make a change to your code or merge it with other branches.
If you have your tests automated, then you can also use that to calculate code coverage. By calculating the code coverage, you can put a requirement in your pipeline to ensure that your code coverage stays above a certain percentage. If the coverage drops below that percentage, you can report it as a warning, fail the pipeline, or just send out some sort of alert so that the development team can add more tests around the uncovered code.
The definition of "Technical Debt" is code without automated tests. Making platform, infrastructure, dependency changes without automated tests is very risky.
Testing your code is not enough to ensure its quality and maintainability. You should also review and refactor your code regularly, to identify and fix any issues, such as bugs, code smells, duplication, complexity, or inconsistency. You can use tools such as code analysis, code review, and code formatting to help you with this task. You should also follow the best practices and standards of your language and project, such as naming conventions, coding style, documentation, and comments.
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Before doing any refactor, you should strongly consider having the functionality covered by tests. If the functionality is covered by tests, then you can feel much safer about the refactor knowing that you haven't changed any functionality inadvertently. Naturally, your refactor may require a refactor of the tests too. Just be sure to analyze any test failures very carefully to see if the test needs to be refactored to account for your changes, or if your changes are actually introducing bugs.
The ultimate goal of testing your code is to make sure that it works as intended and meets the expectations and needs of your users. Therefore, you should also test your code with real users, to get feedback and insights on how they use and perceive your product or service. You can use methods such as user testing, usability testing, beta testing, and A/B testing to collect data and opinions from your target audience, and use them to improve your code and user experience.
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