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| 基于机器学习的开源软件项目维护状态识别 |
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罗诗雨1,2, 李馨蕾3, 罗俊韬1, 王新1,2, 张国锋3, 陈阳1,2
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1.复旦大学 计算机科学技术学院, 上海 200438;2.上海市智能信息处理重点实验室(复旦大学), 上海 200438;3.上海对外经贸大学 统计与信息学院, 上海 201620
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| 摘要: |
| 随着开源软件的广泛普及和迅速发展, 对开源软件项目的维护工作成为软件开发周期中的一个关键环节. 作为全球范围内代表性的开发者社区, GitHub往往在同一领域有着大量功能相似的软件项目仓库, 导致用户在选择合适的项目仓库进行使用或进一步开发时面临挑战, 因此协助用户准确识别项目仓库的维护状态具有重要的现实意义. 然而, GitHub平台并未提供可以直接衡量项目仓库维护状态的信息. 提出一个基于机器学习的项目仓库维护状态自动识别方法, 设计实现一套基于机器学习的分类模型GitMT, 通过有效整合动态时间序列特征和描述性特征, 可以实现项目仓库“活跃”与“未维护”状态的准确识别. 经过一系列基于大规模真实数据的实验验证, GitMT在项目仓库维护状态的识别任务中AUC值达到了0.964. 此外, 还构建一个以软件项目仓库维护状态为中心的开源数据集——GitMT Dataset: https://doi.org/10.7910/DVN/OJ2NI3. |
| 关键词: 维护状态识别 开源软件项目 机器学习 动态时间序列特征 |
| DOI：10.13328/j.cnki.jos.007380 |
| 分类号:TP311 |
| 基金项目:国家自然科学基金(62072115, 62472101); 上海市“科技创新行动计划”政府间国际科技合作项目(22510713600); 上海市“科技创新行动计划”启明星项目(扬帆专项) (22YF1415000); 上海市“科技创新行动计划”社会发展科技攻关项目(22dz1204900) |
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| Identification of Maintenance Status in Open-source Software Projects Based on Machine Learning |
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LUO Shi-Yu1,2, LI Xin-Lei3, LUO Jun-Tao1, WANG Xin1,2, ZHANG Guo-Feng3, CHEN Yang1,2
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1.School of Computer Science, Fudan University, Shanghai 200438, China;2.Shanghai Key Laboratory of Intelligent Information Processing (Fudan University), Shanghai 200438, China;3.School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China
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| Abstract: |
| With the widespread adoption and rapid advancement of open-source software, the maintenance of open-source software projects has become a critical phase within the software development cycle. As a globally representative developer community, GitHub hosts numerous software project repositories with similar functionalities within the same domain, creating challenges for users when selecting the appropriate project repository for use or further development. Therefore, accurate identification of project repository maintenance status holds substantial practical value. However, the GitHub platform does not provide direct metrics for assessing the maintenance status of repositories. This study proposes an automatic identification method for project repository maintenance status based on machine learning. A classification model, GitMT, has been developed and implemented to achieve this objective. By effectively integrating dynamic time series features and descriptive features, the proposed model enables accurate identification of “active” and “unmaintained” repository status. Through a series of experiments conducted on large-scale real-world data, an AUC value of 0.964 is achieved in maintenance status identification tasks. In addition, this study constructs an open-source dataset centered on the maintenance status of software project repositories—GitMT Dataset: https://doi.org/10.7910/DVN/OJ2NI3. |
| Key words: maintenance status recognition open-source software project machine learning dynamic time series feature |
