5 Best Practices to Improve Code Coverage in Automated Testing

Code coverage is certainly one of the most frequently debated measures of software quality in development and testing. Code coverage provides an additional way for teams to see how much of the source code is being executed during testing and can help uncover areas of the code that have not been tested which may harbor defects. High code coverage does not guarantee that the software is bug-free, but it certainly is a strong signal of the effectiveness of tests performed, as well as an predictor of product quality. To increase code coverage in automated testing, it takes more than just writing a set of additional test cases. Increasing code coverage is a structured endeavor, requires automation to be utilized effectively and be integrated into a development process. This paper provides an overview of five best practices for increasing code coverage through automated testing to enhance quality. 

Best Practices to Improve Code Coverage

1. Write Meaningful Unit Tests: Having meaningful unit tests is the first step in building code coverage. Software developers need to write unit tests that exercise critical functions, edge cases and error handling paths, not just the "happy flow." Effective unit tests will help identify hidden issues earlier in the development cycle and ensure that every relevant line of code is exercised. Branch coverage, for example, requires that both the true and false conditions of decision statements be validated. Branch coverage helps prevent cases when, despite having high overall coverage, one branch of a decision remains untested.

2. Embed Code Coverage Tools Within CI/CD Pipelines: Tracking coverage manually is inefficient process, instead use code coverage analysis tools integrated into CI/CD pipelines for visibility throughout development phase, not only after the fact. These tools will automatically generate a report that can highlight the areas that lack coverage, as well as the overall percentage of team coverage, after each build. Tools like JaCoCo, Istanbul, and Cobertura provide finer details to the number of lifts in area of statement, branch, and path coverage. In addition, code coverage tools will enforce quality gates in the CI/CD pipelines and prevent blending code unless the minimum coverage threshold has been fulfilled.

3. Focus on Critical Paths Over 100% Coverage: Shotgunning towards 100% code coverage usually ends up being wasted time and effort, and any test cases that were developed as a result are wasted words. Instead, teams should focus on the most critical paths of the business logic, modalities where security is a concern, and most frequently accessed functionalities. In essence, risk-based testing commits limited team resources to cover problems at the highest risk because this approach contracts the time spent in locating and fixing bugs. Thinking critically about this approach, meeting 70 - 80% coverage on the Organization's critical paths will usually provide way more value than meeting 100% coverage on unimportant "trivial" code.

4. Leverage Automated Test Generation to Improve Speed of Coverage: Tools that utilize automated test generation via AI and machine learning are becoming more common. These automated testing tools can review application code and automatically generate test cases that fill gaps in coverage that developers may have unintentionally left out. Automatically creating tests will hasten test generation, improve coverage, and not require any manual effort. Automated test generation has the benefit of reducing human error, but it has the added benefit as the codebase is being changed, it will help to maintain coverage. Ultimately, this can lead to shorter feedback cycles and more robust applications.

5. Practice Continuous Refactoring and Test Maintenance: Outdated and/or duplicated test cases can lead to inaccurate coverage metrics as applications change over time. By continuously refactoring the test cases you will make sure your tests are with the current state of the code base. Continuously removing tests while making sure you have updated coverage reports will constantly improve the ongoing process of testing with respect to maintaining accuracy of coverage.

How Keploy Can Bring Value to Increasing Code Coverage 

A challenge in increasing code coverage is creating tests that are meaningful and realistic. Keploy generates unit and integration tests by recording the actual API calls and responses the developer used during development. So unlike typical test frameworks, Keploy actually validates functionality against real scenarios, enabling various teams to realize higher coverage with minimal work. Not only does it minimize manual test creation, using real data-driven cases, but it also gives ability for QA teams to push speed and maximize quality.

Conclusion

Increasing code coverage has nothing to do with hitting an arbitrary number, but rather creating testing that is meaningful, efficient, and aligned to business outcomes. Teams will be able to create efficiencies and improve coverage, and cover their software quality, by writing meaningful unit tests, integrating automation testing tools into CI/CD, focusing on critical paths, using automation testing solutions and keeping their tests current through refactoring. Code coverage should be treated as a guiding metric rather than a target. It serves more purpose when consciously designed and automated, and is thus a vital cog in delivering high quality, reliable and high-performing applications.