Clustering support for automated tracing

Automated trace tools dynamically generate links between various software artifacts such as requirements, design elements, code, test cases, and other less structured supplemental documents. Trace algorithms typically utilize information retrieval methods to compute similarity scores between pairs of artifacts. Results are returned to the user as a ranked set of candidate links, and the user is then required to evaluate the results through performing a top-down search through the list. Although clustering methods have previously been shown to improve the performance of information retrieval algorithms by increasing understandability of the results and minimizing human analysis effort, their usefulness in automated traceability tools has not yet been explored. This paper evaluates and compares the effectiveness of several existing clustering methods to support traceability; describes a technique for incorporating them into the automated traceability process; and proposes new techniques based on the concepts of theme cohesion and coupling to dynamically identify optimal clustering granularity and to detect cross-cutting concerns that would otherwise remain undetected by standard clustering algorithms. The benefits of utilizing clustering in automated trace retrieval are then evaluated through a case study