Despite these challenges, the authors believe that AI can help developers generate test candidates and increase code coverage more efficiently. They envision Cover-Agent running automatically for every pre/post-pull request, suggesting validated regression test enhancements. They invite collaboration on the open-source Cover-Agent repo on GitHub and are seeking a good benchmark for tools like this for further development and research.
Key takeaways:
- Meta researchers have developed a tool called TestGen-LLM, an automated approach to increasing test coverage in software engineering. However, the code for TestGen-LLM was not released.
- An open-source version of TestGen-LLM, called Cover-Agent, has been implemented and released. It generates, validates, and proposes as many tests as possible until achieving the coverage requirement, without requiring manual intervention throughout the process.
- While implementing and reviewing TestGen-LLM, several challenges were encountered, including issues with languages that use significant whitespace, the need for additional context for unit test generation, and the inability to add library imports when extending an existing test suite.
- The vision for Cover-Agent is to run automatically for every pre/post-pull request and automatically suggest regression test enhancements that have been validated to work and increase code coverage.