Abstract
Recently, different methods and tools have been proposed to automate or semi-automate test-to-code traceability recovery. Among these, Slicing and Coupling based Test to Code trace Hunter (SCOTCH) exploits slicing and conceptual coupling to identify the classes tested by a JUnit test. However, until now the evaluation of test-to-code traceability recovery methods has been limited to experiments assessing their tracing accuracy rather than the actual support these methods provide to a software engineer during traceability recovery tasks. Indeed, a research method or tool has a better chance of being transferred to practitioners if it is supported by empirical evidence. In this paper, we present the results of two controlled experiments carried out to evaluate the support given by SCOTCH during traceability recovery, when compared with other traceability recovery methods. The results show that SCOTCH is able to suggest a higher number of correct links with higher accuracy, thus sensibly improving the performances of software engineers during test-to-code traceability recovery tasks