一句话：提出熵指标量化开源开发者社交网络中社区分裂、收缩、合并、扩张等演化程度，并验证其有效性与预测价值。
现有研究常用分裂、收缩、合并、扩张、出现、消亡等模式描述开源软件（OSS）开发者社交网络（DSN）中的社区演化，但基于模式的方法难以定量分析，且常将不同模式视为互斥。本文提出不同模式在每对社区演化中可同时发生、只是程度不同；设计四个基于熵的指数，分别度量社区分裂、收缩、合并和扩张的程度，并具备单调性、有意义的上下界等性质，还可组合描述出现和消亡等模式。作者在真实 OSS 项目中评估，发现指标能有效刻画社区演化，与现有方法检测演化模式的一致准确率达 94.1%，并可用于预测 OSS 团队生产力，准确率为 0.718。该工作首次量化不同模式下的社区演化程度。
Understanding the evolution of communities in developer social networks (DSNs) around open source software (OSS) projects can provide valuable insights about the socio-technical process of OSS development. Existing studies show the evolutionary behaviors of social communities can effectively be described using patterns including split, shrink, merge, expand, emerge, and extinct. However, existing pattern-based approaches are limited in supporting quantitative analysis, and are potentially problematic for using the patterns in a mutually exclusive manner when describing community evolution. In this work, we propose that different patterns can occur simultaneously between every pair of communities during the evolution, just in different degrees. Four entropy-based indices are devised to measure the degree of community split, shrink, merge, and expand, respectively, which can provide a comprehensive and quantitative measure of community evolution in DSNs. The indices have properties desirable to quantify community evolution including monotonicity, and bounded maximum and minimum values that correspond to meaningful cases. They can also be combined to describe more patterns such as community emerge and extinct. We conduct studies with real-world OSS projects to evaluate the validity of the proposed indices. The results suggest the proposed indices can effectively capture community evolution, and are consistent with existing approaches in detecting evolution patterns in DSNs with an accuracy of 94.1%. The results also show that the indices are useful in predicting OSS team productivity with an accuracy of 0.718. In summary, the proposed approach is among the first to quantify the degree of community evolution with respect to different patterns, which is promising in supporting future research and applications about DSNs and OSS development.
DOI 原文 ·
@inproceedings{paperbot7031,
title = {Quantifying community evolution in developer social networks},
author = {Liang Wang and Ying Li and Jierui Zhang and Xianping Tao},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3549106}
}