The difference between code coverage and code quality
Code Coverage
Sep 20, 2023
In software development, it’s not uncommon to have to read and work with a disorganized codebase. Observing metrics like code coverage and code quality are paramount to writing durable and scalable software. As your source code becomes increasingly intricate, understanding the nuances of unit testing, branch coverage, and other testing metrics becomes crucial for maintaining high standards in code quality and testing practices. This article aims to explore these concepts and their significance in modern software development.
Exploring Code Coverage Metrics
Software testing is quantified using metrics like code coverage, which measures the extent to which your test suite interacts with the application code, helping identify untested sections. Seems easy enough! However, there are various types of code coverage, which include statement coverage, branch coverage, and function coverage. Statement coverage focuses on the execution of individual lines of code, while branch coverage delves into the true and false branches within conditional statements. Function coverage assesses the percentage of functions or methods exercised by your tests.
Code coverage tools, such as JaCoCo and coverage.py, provide quantitative insights into the areas of the codebase touched by test cases. Achieving high code coverage, while important, should be balanced with other testing techniques and priorities, like integration testing and acceptance testing.
Code Quality: Beyond the Metrics
Code quality is a necessity when creating software applications. It encompasses readability, maintainability, security, and more. A well-maintained codebase adheres to consistent coding conventions, making it accessible to developers across programming languages like Python and Java. It ensures that lines of code are organized, commented, and well-structured. A high code quality not only minimizes bugs but also enables efficient debugging and future enhancements.
Striving for code quality requires a holistic approach. Test-driven development (TDD) is a technique that combines testing and coding. It involves writing test cases before writing the actual application code, ensuring that each feature or function is rigorously tested. TDD aids in achieving both high code coverage and code quality, leading to fewer instances of dead code – code that is not executed during runtime.
Balancing Code Coverage and Code Quality
Code coverage and code quality are not adversaries; they are allies in the pursuit of software excellence. While code coverage metrics provide insights into the extent of testing, code quality metrics highlight the maintainability and robustness of the application. Consider the following tips for achieving a harmonious balance:
Prioritizing Test Cases: Focus on testing functional requirements and critical areas of the codebase while ensuring high test coverage.
Integration of Testing Techniques: Combine different types of testing, such as white-box and black-box testing, to comprehensively evaluate the software application.
Embrace Test Automation: Leverage automation testing to run tests frequently, ensuring that new features and changes are consistently tested.
Use Code Coverage Tools: Harnessing the power of code coverage tools like BuildPulse Coverage make tracking code coverage simple, allowing you to focus on code quality. With their tool, you can track code coverage, pinpointing both tested and untested lines of code. BuildPulse Coverage also empowers you to enforce coverage standards across your team, ensuring that technical debt is minimized and software quality is upheld.
Collaboration: Encourage collaboration between developers, testers, and stakeholders to collectively enhance the software requirements coverage.
Continual Refinement: Regularly revisit and update existing tests and application code to maintain the balance between quality and coverage.
Conclusion
In the world of software development, metrics such as code coverage and code quality act as guiding stars, illuminating the path to software excellence. However, not all parts of your codebase are equal - some codepaths are more critical than others. Some codepaths run more than others. BuildPulse helps you measure and enforce code coverage in a granular way - helping you identify blind spots and enforce code coverage differently, for different parts of your codebase.
As applications grow in complexity, understanding how testing metrics like line coverage, statement coverage, branch coverage, and function coverage contribute to code quality becomes indispensable. Striving for high code coverage while prioritizing code quality ensures that software applications not only fulfill functional requirements but are also resilient, maintainable, and accessible. Ultimately, the harmony between code coverage and code quality leads to software that stands the test of time.
FAQ
What is the difference between a flaky test and a false positive?
A false positive is a test failure in your test suite due to an actual error in the code being executed, or a mismatch in what the test expects from the code.
A flaky test is when you have conflicting test results for the same code. For example, while running tests if you see that a test fails and passes, but the code hasn’t changed, then it’s a flaky test. There’s many causes of flakiness.
What is an example of a flaky test?
An example can be seen in growing test suites - when pull request builds fail for changes you haven’t made. Put differently, when you see a test pass and fail without any code change. These failed tests are flaky tests.
What are common causes of flakiness?
Broken assumptions in test automation and development process can introduce flaky tests - for example, if test data is shared between different tests whether asynchronous, high concurrency, or sequential, the results of one test can affect another.
Poorly written test code can also be a factor. Improper polling, race conditions, improper event dependency handling, shared test data, or timeout handling for network requests or page loads. Any of these can lead to flaky test failures and test flakiness.
End-to-end tests that rely on internal API uptime can cause test flakiness and test failures.
What's the impact of flaky tests?
Flaky tests can wreck havoc on the development process - from wasted developer time from test retries, to creating bugs and product instability and missed releases, time-consuming flaky tests can grind your development process to a halt.
What is the best way to resolve or fix flaky tests?
Devops, software engineering, and software development teams will often need to compare code changes, logs, and other context across test environments from before the test instability started, and after - adding retries or reruns can also help with debugging. Test detection and test execution tooling can help automate this process as well.
BuildPulse enables you to find, assess impact metrics, quarantine, and fix flaky tests.
What are some strategies for preventing flaky tests?
Paying attention and prioritizing flaky tests as they come up can be a good way to prevent them from becoming an issue. This is where a testing culture is important - if a flaky test case is spotted by an engineer, it should be logged right away. This, however, takes a certain level of hygiene - BuildPulse can provide monitoring so flaky tests are caught right away.
What type of tests have flaky tests?
Flaky tests can be seen across the testing process - unit tests, integration tests, end-to-end tests, UI tests, acceptance tests.
What if I don't have that many flaky tests?
Flaky tests can be stealthy - often ignored by engineers and test runs are retried, they build up until they can’t be ignored anymore. These automated tests slow down developer productivity, impact functionality, and reduce confidence in test results and test suites. Better to get ahead while it’s easy and invest in test management.
It’s also important to prevent regressions to catch flakiness early while it’s manageable.
What languages and continuous integration providers does BuildPulse work with?
BuildPulse integrates with all continuous integration providers (including GitHub Actions, BitBucket Pipelines, and more), test frameworks, and workflows.
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How long does implementation/integration with BuildPulse take?
Implementation/integration takes 5 minutes!