A STUDY OF SEMI-AUTOMATED TRACING

Requirements tracing is crucial for software engineering practices including change analysis, regression testing, and reverse engineering. The requirements tracing process produces a requirements traceability matrix(TM) which links high- and low-level document elements. Manually generating a TM is laborious, time consuming, and error-prone. Due to these challenges TMs are often neglected. Automated information retrieval(IR) techniques are used with some efficiency. However, in mission- or safety-critical systems a human analyst is required to vet the candidate TM. This introduces semi-automated requirements tracing, where IR methods present a candidate TM and a human analyst validates it, producing a final TM. In semi-automated tracing the focus becomes the quality of the final TM. This thesis expands upon the research of Cuddeback et al. by examining how human analysts interact with candidate TMs. We conduct two experiments, one using an automated tracing tool and the other using manual validation. We conduct formal statistical analysis to determine the key factors impacting the analyst’s tracing performance. Additionally, we conduct a pilot study investigating how analysts interact with TMs generated by automated IR methods. Our research statistically confirms the finding of Cuddeback et al. that the strongest impact on analyst performance is the initial TM quality. Finally we show evidence that applying local filters to IR results produce the best candidate TMs.