一句话:基于 150 万+ issue 的实证研究,揭示 GitHub 标签在优先级排序中的局限,并比较多种排序算法。
本文关注 GitHub 上大型开源项目中 issue 优先级排序效率低、机制不清的问题。作者基于涵盖多种项目、超过 150 万个 issue 的数据集开展实证研究,考察标签在 issue 优先级排序中的作用,分析各类 issue 特征对排序的影响,并比较不同排序算法在这些有效特征上的表现。研究发现标签对优先级排序的效果有限,某些 issue 特征具有显著影响,不同排序算法性能存在差异。该工作为开源平台的 issue 处理提供经验依据,并为后续自动化优先级排序研究提供指导。
GitHub has become a prominent platform for open source software development, facilitating collaboration and communication among a diverse group of contributors. Efficient issue tracking is a crucial aspect of managing projects on GitHub, and labels serve as one of the primary mechanisms for issue prioritization, while various other issue features are also utilized by issue handlers for the same purpose. However, in large projects, prioritizing issues remains a challenge, and the efficacy of using labels or other issue features for prioritization is not well understood. To address this knowledge gap, we conduct a comprehensive empirical study that investigates the role of labels in GitHub issue prioritization, examines the influence of various issue features on prioritization, and assesses the performance of different ranking algorithms based on these impactful features. Our study, conducted on a dataset comprising data from over 1.5 million issues across diverse GitHub projects, provides valuable insights for issue handling in open source platforms and offers guidance for future research in this domain. Specifically, the study reveals the limited effectiveness of labels in issue prioritization, highlights the significance of certain issue features in the prioritization process, and compares the performance of various ranking algorithms for issue prioritization to support issue handlers.