| 摘要: |
| 群体智能是新一代人工智能的重要组成部分, 研究如何激发和汇聚开源社区“人”的力量能够极大提升开发效率. 社区检测作为研究开源项目中开发者关系的技术手段, 在社交网络的探索和理解中起着至关重要的作用. 然而, 目前的研究主要集中在Facebook等大规模社交网络上, 系统性地研究项目级开源软件开发者社交网络(OSS-DSN)中的社区检测仍然较为缺乏. 首先收集真实世界的数据, 并分析OSS-DSN的特征. 然后, 在这些真实数据集上, 对多种重叠和非重叠的社区检测算法进行基准测试, 比较它们在多个度量和维度上的性能. 最后, 基于合成的OSS-DSN, 高效地生成了一系列合成网络, 并使用带有真实标签的数据进行算法评估, 以进行对比分析. 识别了小规模和中等规模社交网络与大规模网络之间的特征差异, 并探讨了这些差异如何影响社区检测的度量标准及算法性能. 提供了一个新的基准, 并为更好地理解开源软件社区中的沟通与协作提供了重要的见解. |
| 关键词: 开源软件 开发者社交网络 社区检测 |
| DOI：10.13328/j.cnki.jos.007555 |
| 分类号:TP311 |
| 基金项目:国家自然科学基金面上项目(62172203) |
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| Benchmarking Study of Community Detection on Developer Communication Networks in Real Open Source Software Repositories |
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ZHANG Jie-Rui1, WANG Liang1, CHANG Zheng-Chao2, LI Zi-Rong1, SANG Bai-Hui1, TAO Xian-Ping1
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1.Frontier Interdisciplinary Center, Institute of Computer Software, Nanjing University, Nanjing 210023, China;2.School of Information Science & Technology, Northwest University, Xi’an 710127, China
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| Abstract: |
| Crowd intelligence is a crucial component of the next generation of artificial intelligence. Researching how to stimulate and converge the innovative power of “people” in open-source communities can significantly enhance development efficiency. Community detection, as a technical approach to studying the relationships among developers in open-source projects, plays a vital role in exploring and understanding social networks. However, current research has predominantly focused on large-scale social networks such as Facebook, while systematic studies on community detection in project-level open source software developer social networks (OSS-DSN) remain limited. This study first collects real-world data and analyzes the features of OSS-DSN. Then, it benchmarks several overlapping and non-overlapping community detection algorithms on these real datasets, comparing algorithm performance across multiple metrics and dimensions. Finally, based on synthetic OSS-DSN, it generates networks efficiently and performs algorithm evaluations using ground-truth data for comparative analysis. Differences in characteristics between small- and medium-scale social networks and large-scale networks are identified, and the influence of these differences on community detection metrics and algorithm performance is explored. The study provides a new benchmark and offers important insights into communication and collaboration in open-source software communities. |
| Key words: open source software (OSS) developer social network (DSN) community detection |
