Comparing and combining evolutionary couplings from interactions and commits

Abstract

The paper presents an approach to mine evolutionary couplings from a combination of interaction (e.g., Mylyn) and commit (e.g., CVS) histories. These evolutionary couplings are expressed at the file and method levels of granularity, and are applied to support the tasks of commit and interaction predictions. Although the topic of mining evolutionary couplings has been investigated previously, the empirical comparison and combination of the two types from interaction and commit histories have not been attempted. An empirical study on 3272 interactions and 5093 commits from Mylyn, an open source task management tool, was conducted. These interactions and commits were divided into training and testing sets to evaluate the combined, and individual, models. Precision and recall metrics were used to measure the performance of these models. The results show that combined models offer statistically significant increases in recall over the individual models for change predictions. At the file level, the combined models achieved a maximum recall improvement of 13% for commit prediction with a 2% maximum precision drop.