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

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.