Measuring and Mining Community Evolution in Developer Social Networks with Entropy-Based Indices

This work presents four novel entropy-based indices for measuring the community evolution of developer social networks (DSNs) in open source software (OSS) projects. The proposed indices offer a quantitative measure of community split, shrink, merge, and expand events. The indices have proven properties like monotonicity, and they have defined maximum and minimum values that signify meaningful scenarios. These indices can be combined to describe complex community evolution events such as emergence and extinction. Expanding upon these indices, this research proposes a novel machine learning approach, leveraging shapelet mining, to unearth representative patterns of community evolution. The results from real-world OSS projects show that these indices effectively capture various community evolution behaviors with a 94.1% accuracy compared to existing work. They also predict OSS team productivity with a 0.718 accuracy. With the shapelet mining and learning framework, the indices can identify patterns of community evolution and predict the survival of OSS projects with 93% accuracy 3 months before the projects' last observed commits. The findings highlight the potential of these entropy-based indices for understanding OSS project status and predicting future trends, which are valuable for supporting future research on DSNs and OSS communities.