Grouping Semantically Related Change-Sets to Enhance Identification of Logical Coupling
Neeraj Mathur, Sai Anirudh Karre, Y. Raghu Reddy · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2019
Identifying dependency between various artifacts in a large scale software system is a non-trivial task.As the software evolves, multiple artifacts like files, docs, classes, database scripts, etc., are likely to undergo change concurrently.Such artifacts tend to have a dependency between them, otherwise referred to as logical coupling.Researchers have used Support and Confidence as an association rule based measurement to predict the levels of logical coupling among the software artifacts.However, employing a single change on a software artifact can span across various closely related changes when many code contributors are working on the same change.Thus it is important to preprocess and group these semantically related change-sets before identifying logical coupling.In this paper, we propose a method to identify logical coupling and group semantically related change sets.We evaluate our method on real-world git repositories and document our observations.