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Tag Archives: repositories

GitHub now uses AI to recommend open issues in project repositories

January 22, 2020   Big Data

Large open source projects on GitHub have intimidatingly long lists of problems that require addressing. To make it easier to spot the most pressing, GitHub recently introduced the “good first issues” feature, which matches contributors with issues that are likely to fit their interests. The initial version, which launched in May 2019, surfaced recommendations based on labels applied to issues by project maintainers. But an updated release shipped last month incorporates an AI algorithm that GitHub claims surfaces issues in about 70% of repositories recommended to users.

GitHub notes that it’s the first deep-learning-enabled product to launch on Github.com.

According to GitHub senior machine learning engineer Tiferet Gazit, GitHub last year conducted an analysis and manual curation to create a list of 300 label names used by popular open source repositories. (All were synonyms for either “good first issue” or “documentation,” like “beginner friendly,” “easy bug fix,” and “low-hanging-fruit.”) But relying on these meant that only about 40% of the recommended repositories had issues that could be surfaced. Plus, it left project maintainers with the burden of triaging and labeling issues themselves.

The new AI recommender system is largely automatic, by contrast. But building it required crafting an annotated training set of hundreds of thousands of samples.

 GitHub now uses AI to recommend open issues in project repositories

GitHub began with issues that had any of the roughly 300 labels in the curated list, which it supplemented with a few sets of issues that were also likely to be beginner-friendly. (This included those that were closed by a user who had never previously contributed to the repository, as well as issues closed that touched only a few lines of code in a single file.) After detecting and removing near-duplicate issues, several training, validation, and test sets were separated across repositories to prevent data leakage from similar content, and GitHub trained the AI system using only preprocessed and denoised issue titles and bodies to ensure it detected good issues as soon as they’re opened.

In production, each issue for which the AI algorithm predicts a probability above the required threshold is slated for recommendation, with a confidence score equal to its predicted probability. Open issues from non-archived public repositories that have at least one of the labels from the curated label list are given a confidence score based on the relevance of their labels, with synonyms of “good first issue” awarded higher confidence than synonyms of “documentation.” At the repository level, all detected issues are ranked primarily based on their confidence score (though label-based detections are generally given higher confidence than ML-based detections), along with a penalty on issue age.

Data acquisition, training, and inference pipelines run daily, according to Gazit, using scheduled workflows to ensure the results remain “fresh” and “relevant.” In the future, GitHub intends to add better signals to its repository recommendations and a mechanism for maintainers and triagers to approve or remove AI-based recommendations in their repositories. And it plans to extend issue recommendations to offer personalized suggestions on next issues to tackle for anyone who has already made contributions to a project.

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GitHub releases data on 2.8 million open source repositories through Google BigQuery

June 30, 2016   Big Data

GitHub today announced that it’s releasing activity data for 2.8 million open source code repositories and making it available for people to analyze with the Google BigQuery cloud-based data warehousing tool.

The data set is free to explore. (With BigQuery you get to process up to one terabyte each month free of charge.)

This new 3TB data set includes information on “more than 145 million unique commits, over 2 billion different file paths and the contents of the latest revision for 163 million files, all of which are searchable with regular expressions,” Arfon Smith, program manager for open source data at GitHub, wrote in a blog post.

To get people started, Smith has put together some starter queries. Felipe Hoffa, a Google developer advocate who focuses on BigQuery, has put together some tips for working with the data sets in a Medium post.

The data set could be useful to anyone who want to get a sense of trends in open source software use on GitHub, and it’s simpler than tinkering with the GitHub application programming interface (API). For sure, GitHub, with more than 15 million users, isn’t the only place where open source software lives on the Internet — see also GitLab — but it is a very popular one, perhaps the most popular.

Today’s move effectively amounts to an expansion of the GitHub Archive, which was first introduced by Google web performance engineer Ilya Grigorik in 2012.

GitHub will update the data set every week, a spokesperson told VentureBeat in an email.

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