Enriching reverse engineering with semantic clustering

Understanding a software system by just analyzing the structure of the system reveals only half of the picture, since the structure tells us only how the code is working but not what the code is about. What the code is about can be found in the semantics of the source code: names of identifiers, comments etc. In this paper, we analyze how these terms are spread over the source artifacts using latent semantic indexing, an information retrieval technique. We use the assumption that parts of the system that use similar terms are related. We cluster artifacts that use similar terms, and we reveal the most relevant terms for the computed clusters. Our approach works at the level of the source code which makes it language independent. Nevertheless, we correlated the semantics with structural information and we applied it at different levels of abstraction (e.g. classes, methods). We applied our approach on three large case studies and we report the results we obtained.