Journal of Computer Science and Technology ›› 2022, Vol. 37 ›› Issue (1): 182-206.doi: 10.1007/s11390-021-1596-1

Special Issue: Software Systems

• Special Section on Software Systems 2021 • Previous Articles     Next Articles

Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics

Zi-Jie Huang1 (黄子杰), Student Member, CCF, IEEE, Zhi-Qing Shao1,* (邵志清), Gui-Sheng Fan1,2,* (范贵生), Member, CCF, Hui-Qun Yu1,3 (虞慧群), Senior Member, CCF, IEEE, Member, ACM, Xing-Guang Yang1 (杨星光), and Kang Yang1 (杨康)        

  1. 1Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
    2Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai 200237, China
    3Shanghai Engineering Research Center of Smart Energy, Shanghai 200237, China
  • Received:2021-05-18 Revised:2021-11-01 Accepted:2022-01-10 Online:2022-01-28 Published:2022-01-28
  • Contact: Zhi-Qing Shao, Gui-Sheng Fan E-mail:{zshao, gsfan}@ecust.edu.cn
  • About author:Zhi-Qing Shao received his B.S. degree in mathematical logic from Nanjing University, Nanjing, in 1986, M.S. degree in pure mathematics from the Institute of Software, Chinese Academy of Sciences, Beijing, in 1989, and Ph.D. degree in computer software from Shanghai Jiao Tong University, Shanghai, in 1998. He is currently a professor at East China University of Science and Technology, Shanghai. His research interests include software engineering and network computing.
    Gui-Sheng Fan received his B.S. degree from Anhui University of Technology, Ma'anshan, in 2003, M.S. degree from East China University of Science and Technology (ECUST), Shanghai, in 2006, and Ph.D. degree from ECUST in 2009, all in computer science. He is presently a research assistant of the Department of Computer Science and Engineering, ECUST, Shanghai. His research interests include formal methods for complex software system, service-oriented computing, and techniques for analysis of software architecture.
  • Supported by:
    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.

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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 R2 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.

Key words: community smell; developer sentiment; socio-technical analysis; empirical software engineering;

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