Supporting Change Impact Analysis Using a Recommendation System: An Industrial Case Study in a Safety-Critical Context
2017 (English)In: IEEE Transactions on Software Engineering, ISSN 0098-5589, E-ISSN 1939-3520, Vol. 43, no 7, p. 675-700, article id 7637029Article in journal (Refereed) Published
Abstract [en]
Change Impact Analysis (CIA) during software evolution of safety-critical systems is a labor-intensive task. Several authors have proposed tool support for CIA, but very few tools were evaluated in industry. We present a case study on ImpRec, a recommendation System for Software Engineering (RSSE), tailored for CIA at a process automation company. ImpRec builds on assisted tracing, using information retrieval solutions and mining software repositories to recommend development artifacts, potentially impacted when resolving incoming issue reports. In contrast to the majority of tools for automated CIA, ImpRec explicitly targets development artifacts that are not source code. We evaluate ImpRec in a two-phase study. First, we measure the correctness of ImpRec's recommendations by a simulation based on 12 years' worth of issue reports in the company. Second, we assess the utility of working with ImpRec by deploying the RSSE in two development teams on different continents. The results suggest that ImpRec presents about 40 percent of the true impact among the top-10 recommendations. Furthermore, user log analysis indicates that ImpRec can support CIA in industry, and developers acknowledge the value of ImpRec in interviews. In conclusion, our findings show the potential of reusing traceability associated with developers' past activities in an RSSE.
Place, publisher, year, edition, pages
2017. Vol. 43, no 7, p. 675-700, article id 7637029
Keywords [en]
Case, maintenance management, software and system safety, tracing, Accident prevention, Computer aided software engineering, Safety engineering, Software engineering, Change impact analysis, Industrial case study, Mining software repositories, Safety critical systems, Software and system safeties, Software Evolution, Recommender systems
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-33200DOI: 10.1109/TSE.2016.2620458Scopus ID: 2-s2.0-85029315080OAI: oai:DiVA.org:ri-33200DiVA, id: diva2:1179234
Note
Funding details: Sweden-Japan Foundation; Funding details: Knowledge Foundation; Funding details: Israeli Centers for Research Excellence; Funding details: 20140218, Israeli Centers for Research Excellence; Funding details: Lunds Universitet
2018-01-312018-01-312025-09-23Bibliographically approved