GHALogs: Large-Scale Dataset of GitHub Actions Runs

In recent years, continuous integration and deployment (CI/CD) has become increasingly popular in both the open-source community and industry. Evaluating CI/CD performance is a critical aspect of software development, as it not only helps minimize execution costs but also ensures faster feedback for developers. Despite its importance, there is limited fine-grained knowledge about the performance of CI/CD processes—knowledge that is essential for identifying bottlenecks and optimization opportunities.Moreover, the availability of large-scale, publicly accessible datasets of CI/CD logs remains scarce. The few datasets that do exist are often outdated and lack comprehensive coverage. To address this gap, we introduce a new dataset comprising 116k CI/CD workflows executed using GitHub Actions (GHA) across 25k public code projects spanning 20 different programming languages.This dataset includes 513k workflow runs encompassing 2.3 million individual steps. For each workflow run, we provide detailed metadata along with complete run logs. To the best of our knowledge, this is the largest dataset of CI/CD runs that includes full log data. The inclusion of these logs enables more in-depth analysis of CI/CD pipelines, offering insights that cannot be gleaned solely from code repositories.We postulate that this dataset will facilitate future CI/CD pipeline behavior research through log-based analysis. Potential applications include performance evaluation (e.g., measuring task execution times) and root cause analysis (e.g., identifying reasons for pipeline failures).

近年来，持续集成与持续部署（CI/CD）在开源社区与工业界均日益普及。评估CI/CD性能是软件开发的关键环节，其不仅有助于降低执行成本，还能为开发者提供更及时的反馈。尽管其重要性不言而喻，但目前针对CI/CD流程性能的细粒度认知仍较为匮乏——而这类认知正是识别性能瓶颈与优化契机的核心依据。此外，大规模可公开获取的CI/CD日志数据集仍较为稀缺，现存的少量相关数据集往往时效性不足且覆盖范围不够全面。为填补这一空白，我们推出了全新数据集：该数据集包含基于GitHub Actions（GHA）运行的11.6万个CI/CD工作流，覆盖2.5万个公开代码项目，涉及20种不同编程语言。该数据集共包含51.3万个工作流运行实例，涵盖230万个独立执行步骤。针对每个工作流运行实例，我们均提供了详细的元数据与完整的运行日志。据我们所知，这是目前规模最大的包含完整日志数据的CI/CD运行数据集。这些日志的加入支持对CI/CD流水线开展更深入的分析，能够提供仅通过代码仓库无法获取的研究视角。我们认为，该数据集将通过基于日志的分析方法，为后续CI/CD流水线行为研究提供助力。其潜在应用场景包括性能评估（如测算任务执行时长）与根因分析（如定位流水线故障成因）。