Detecting and Untangling Composite Commits via Attributed Graph Modeling

During software development, developers tend to tangle multiple concerns into a single commit, resulting in many composite commits. This paper studies the problem of detecting and untangling composite commits, so as to improve the maintainability and understandability of software. Our approach is built upon the observation that both the textual content of code statements and the dependencies between code statements are helpful in comprehending the code commit. Based on this observation, we first construct an attributed graph for each commit, where code statements and various code dependencies are modeled as nodes and edges, respectively, and the textual bodies of code statements are maintained as node attributes. Based on the attributed graph, we propose graph-based learning algorithms that first detect whether the given commit is a composite commit, and then untangle the composite commit into atomic ones. We evaluate our approach on nine C# projects, and the results demonstrate the effectiveness and efficiency of our approach.