CIGAR: Contrastive Learning for GitHub Action Recommendation

GitHub Actions was introduced in 2019 as an integrated solution for CI/CD to automate software development workflow. Since then, it has gained tremendous popularity among developers. In a GitHub Actions workflow, actions refer to custom applications for performing complex but frequently repeated tasks. Actions can be typically found in GitHub Marketplace or public GitHub repositories. Prior studies have already disclosed that developers often reuse actions to reduce double work and improve productivity. However, it is not trivial for developers, especially novices, to figure out which action to reuse due to the large number of actions available and the limited search functionality GitHub Marketplace provides. To address this issue, we propose CIGAR (ContrastIve learning for GitHub Action Recommendation). Given the textual description of a task developers want to execute, CIGAR will recommend the most relevant actions. CIGAR exploits a pre-trained RoBERTa model to convert sequences of words into high-dimensional vector representations, and is fine tuned through a contrastive learning objective. The performance of CIGAR was evaluated on a novel dataset curated based on prior research, and the results demonstrate that CIGAR can reliably recommend actions needed by developers and significantly outperforms the GitHub Marketplace search engine. Our study indicates the promise of employing contrastive learning for GitHub action recommendation. The promising performance achieved can potentially drive a wider adoption of GitHub Actions and facilitate the automation of software development workflows.

I. Introduction

GitHub [1] is the largest social coding platforms, hosting over 330 million software repositories and accommodating over 100 million developers in June 2023. By incorporating features like issue tracking and pull requests into distributed version control tools, GitHub has revolutionized the way developers collaborate [2], [3]. While the adoption of a pull-based development workflow presents many opportunities and benefits, it also raises high requirements for repository maintainers to coordinate the activities during the development process such as internal communication, code review, testing, and merging pull requests [4]. To facilitate the development and collaboration, continuous integration and delivery (CI/CD) tools have been widely adopted to automate a wide range of activities including building, testing, quality assurance [5].