Abstract
The paper presents an approach to perform impact analysis (IA) of an incoming change request on source code. The approach is based on a combination of interaction (e.g., Mylyn) and commit (e.g., CVS) histories. The source code entities (i.e., files and methods) that were interacted or changed in the resolution of past change requests (e.g., bug fixes) were used. Information retrieval, machine learning, and lightweight source code analysis techniques were employed to form a corpus from these source code entities. Additionally, the corpus was augmented with the textual descriptions of the previously resolved change requests and their associated commit messages. Given a textual description of a change request, this corpus is queried to obtain a ranked list of relevant source code entities that are most likely change prone. Such an approach that combines information from interactions and commits for IA at the change request level was not previously investigated. Furthermore, the approach requires only the entities that were interacted and/or committed in the past, which differs from the previous solutions that require indexing of a complete snapshot (e.g., a release).
An empirical study on 3272 interactions and 5093 commits from Mylyn, an open source task management tool, was conducted. The results show that the combined approach outperforms an individual approach based on commits. Moreover, it also outperformed an approach based on indexing a single, complete snapshot of a software system.