Summarizing Evolutionary Trajectory by Grouping and Aggregating relevant code changes

The lifecycle of a large-scale software system can undergo many releases. Each release often involves hundreds or thousands of revisions committed by many developers over time. Many code changes are made in a systematic and collaborative way. However, such systematic and collaborative code changes are often undocumented and hidden in the evolution history of a software system. It is desirable to recover commonalities and associations among dispersed code changes in the evolutionary trajectory of a software system. In this paper, we present SETGA (Summarizing Evolutionary Trajectory by Grouping and Aggregation), an approach to summarizing historical commit records as trajectory patterns by grouping and aggregating relevant code changes committed over time. SETGA extracts change operations from a series of commit records from version control systems. It then groups extracted change operations by their common properties from different dimensions such as change operation types, developers and change locations. After that, SETGA aggregates relevant change operation groups by mining various associations among them. The proposed approach has been implemented and applied to three open-source systems. The results show that SETGA can identify various types of trajectory patterns that are useful for software evolution management and quality assurance.