文献类型：期刊文献

英文题名：Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics

作者：Huang, Zi-Jie[1];Shao, Zhi-Qing[1];Fan, Gui-Sheng[1,2];Yu, Hui-Qun[1,3];Yang, Xing-Guang[1];Yang, Kang[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Key Lab Comp Software Testing & Evaluati, Shanghai 200237, Peoples R China;[3]Shanghai Engn Res Ctr Smart Energy, Shanghai 200237, Peoples R China

年份：2022

卷号：37

期号：1

起止页码：182

外文期刊名：JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY

收录：;EI(收录号：20220911728578);WOS:【SCI-EXPANDED(收录号:WOS:000757837300010)】；

基金：This work was partially supported by the National Natural Science Foundation of China under Grant No. 61772200, and the Natural Science Foundation of Shanghai under Grant No. 21ZR1416300.

语种：英文

外文关键词：community smell; developer sentiment; socio-technical analysis; empirical software engineering

摘要：Community smells are sub-optimal developer community structures that hinder productivity. Prior studies performed smell prediction and provided refactoring guidelines from a top-down aspect to help community shepherds. Simultaneously, refactoring smells also requires bottom-up effort from every developer. However, supportive measures and guidelines for them are not available at a fine-grained level. Since recent work revealed developers' personalities and working states could influence community smells' emergence and variation, we build prediction models with experience, sentiment, and development process features of developers considering three smells including Organizational Silo, Lone Wolf, and Bottleneck, as well as two related classes including smelly developer and smelly quitter. We predict the five classes in the individual granularity, and we also generate forecasts for the number of smelly developers in the community granularity. The proposed models achieve F-measures ranging from 0.73 to 0.92 in individual-wide within-project, time-wise, and cross-project prediction, and mean R-2 performance of 0.68 in community-wide Smelly Developer prediction. We also exploit SHAP (SHapley Additive exPlanations) to assess feature importance to explain our predictors. In conclusion, we suggest developers with heavy workload should foster more frequent communication in a straightforward and polite way to build healthier communities, and we recommend community shepherds to use the forecasting model for refactoring planning.