Enabling traceability reuse for impact analyses: A feasibility study in a safety context

Abstract—Engineers working on safety critical software devel-opment must explicitly specify trace links as part of Impact Anal-yses (IA), both to code and non-code development artifacts. In large-scale projects, constituting information spaces of thousands of artifacts, conducting IA is tedious work relying on extensive system understanding. We propose to support this activity by enabling engineers to reuse knowledge from previously completed IAs. We do this by mining the trace links in documented IA reports, creating a semantic network of the resulting traceability, and rendering the resulting network amenable to visual analyses. We studied an Issue Management System (IMS), from within a company in the power and automation domain, containing 4,845 IA reports from 9 years of development relating to a single safety critical system. The domain has strict process requirements guiding the documented IAs. We used link mining to extract trace links, from these IA reports to development artifacts, and to determine their link semantics. We constructed a semantic network of the interrelated development artifacts, containing 6,104 non-code artifacts and 9,395 trace links, and we used two visualizations to examine the results. We provide initial suggestions as to how the knowledge embedded in such a network can be (re-)used to advance support for IA. Index Terms—impact analysis, issue management, traceability, data mining, semantic networks, visualizatio