GitHub is using artificial intelligence (AI) and machine learning (ML) to recommend open software issues to address first, according to a blog post.

GitHub is a company that offers a version control hosting platform for software projects. The company was looking for a way to make it easier for new users and programmers to be able to contribute to projects. In May 2019, they rolled out their “good first issues” feature, which made recommendations for easy, low-hanging-fruit issues.

The first iteration of the feature relied on project maintainers to label issues. This “led to a list of about 300 label names used by popular open source repositories—all synonyms for either ‘good first issue’ or ‘documentation.’” Ultimately, this could lead to more work, leaving “maintainers with the burden of triaging and labeling issues. Instead of relying on maintainers to manually label their issues, we wanted to use machine learning to broaden the set of issues we could surface.”

As a result, GitHub has introduced a second iteration of the feature, with ML-based, as well as the original label-based, issue recommendations. The end result is that the system now surfaces “good first issues” in approximately 70% of repositories, as opposed to 40% with the first iteration.

GitHub plans on expanding this feature to add “ better signals to our repository recommendations to help users find and get involved with the best projects related to their interests. We also plan to add a mechanism for maintainers and triagers to approve or remove ML-based recommendations in their repositories. Finally, we plan on extending issue recommendations to offer personalized suggestions on next issues to tackle for anyone who has already made contributions to a project.”

The entire blog post is a fascinating read about how AI and ML can be used to transform even mundane tasks.