Uncovering File Relationships using Association Mining and Topic Modeling
Namita Dave, Delmar B. Davis, Karen Potts, Hazeline U. Asuncion · 2014
Software maintenance tasks, such as feature enhancements and bug fixes, require familiarity with the entire software system. A modification task could become very time consuming if there is no prior knowledge of the system. Association mining has been used to identify the files that frequently change together in a software repository, and this information can aid a software engineer locate relevant files for a maintenance task. However, association mining techniques are limited to the amount of project history stored in a software repository. We address this difficulty by using a technique that combines association mining with topic modeling, referred to as Frequent Pattern Growth with Latent Dirichlet Allocation (FP-LDA). Topic modeling aims to uncover file relationships by learning semantic topics from source files. We validated our technique via case studies on two open source projects. Our results indicate that topic modeling can increase the effectiveness of association mining in uncovering the file relationships. Keywords-Association mining; Topic Modeling; Software