Software traceability with topic modeling

TLDR

Software traceability is essential in software engineering, and its automation becomes increasingly critical as projects grow in complexity and artifact volume. The authors propose an automated traceability technique that integrates topic modeling. Their method records traceability links during development, learns a probabilistic topic model over artifacts, and provides semantic categorization and visualization through search, recording, and navigation tools. Applied to multiple datasets, the approach demonstrates that topic modeling improves traceability and that traceability data enriches topic modeling.

Abstract

Software traceability is a fundamentally important task in software engineering. The need for automated traceability increases as projects become more complex and as the number of artifacts increases. We propose an automated technique that combines traceability with a machine learning technique known as topic modeling. Our approach automatically records traceability links during the software development process and learns a probabilistic topic model over artifacts. The learned model allows for the semantic categorization of artifacts and the topical visualization of the software system. To test our approach, we have implemented several tools: an artifact search tool combining keyword-based search and topic modeling, a recording tool that performs prospective traceability, and a visualization tool that allows one to navigate the software architecture and view semantic topics associated with relevant artifacts and architectural components. We apply our approach to several data sets and discuss how topic modeling enhances software traceability, and vice versa.