On the Reruns of GitHub Actions Workflows

GitHub Actions, a built-in CI/CD solution of GitHub, is increasingly popular among developers for automating software development workflows. It has been observed that when automated workflow execution fails, developers sometimes only rerun the workflow or failed job without any modifications to the repository. The widespread use of these reruns has consumed considerable computing resources and raised concerns regarding the reliability and consistency of these workflows. Understanding how developers rerun GitHub Actions workflows and the rationale behind the rerun can provide valuable insights to further improve the reliability and efficiency of the software development process. In this work, we conducted an empirical study on 3,320 open-source Java repositories to understand how developers rerun GitHub Actions workflows and quantify both wasted time and computing resources. We further studied the cases where workflow reruns lead to successful outcomes and manually analyzed the reasons behind the workflow execution flakiness. Based on our findings, we tested four machine learning based models to predict the workflow execution outcome, aiming to reduce the potential resource waste caused by workflow reruns. Our study presents how developers deal with GitHub Actions execution failures, pinpoints root causes of workflow execution flakiness, and offers actionable insights to improve CI/CD workflow reliability and efficiency.