ML experiment: Generate tests for code changes

GitLab is exploring the integration of AI and machine learning into its DevSecOps platform, focusing on enhancing code review processes by utilizing generative AI and large language models to suggest relevant test coverage for changes proposed via merge requests. In a prototype led by Phil Hughes, AI was used to generate test coverage suggestions directly within the merge request interface, offering a new option to provide these suggestions in a sidebar. This initiative aims to bolster confidence in code quality by automatically detecting missing tests and reviewing proposed tests for comprehensiveness. While still experimental, GitLab is iterating on these AI/ML features to improve efficiency across the software development lifecycle, with plans to expand this capability across more tasks and continue sharing developments in an ongoing blog series. GitLab invites interested users to join a waitlist to access these AI-generated features, while emphasizing that information on upcoming products is subject to change.

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