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Supporting Change Impact Analysis Using a Recommendation System: An Industrial Case Study in a Safety-Critical Context
SICS Swedish ICT AB, SWE.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för programvaruteknik. (SERL)
Lund University, SWE. (SERG)
Lund University, SWE. (SERG)
2017 (engelsk)Inngår i: IEEE Transactions on Software Engineering, ISSN 0098-5589, E-ISSN 1939-3520, Vol. 43, nr 7, s. 675-700Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Abstract—Change Impact Analysis (CIA) during software evolution of safety-critical systems is a labor-intensive task. Severalauthors have proposed tool support for CIA, but very few tools were evaluated in industry. We present a case study on ImpRec, arecommendation System for Software Engineering (RSSE), tailored for CIA at a process automation company. ImpRec builds onassisted tracing, using information retrieval solutions and mining software repositories to recommend development artifacts, potentiallyimpacted when resolving incoming issue reports. In contrast to the majority of tools for automated CIA, ImpRec explicitly targetsdevelopment artifacts that are not source code. We evaluate ImpRec in a two-phase study. First, we measure the correctness ofImpRec’s recommendations by a simulation based on 12 years’ worth of issue reports in the company. Second, we assess the utilityof working with ImpRec by deploying the RSSE in two development teams on different continents. The results suggest that ImpRecpresents about 40 percent of the true impact among the top-10 recommendations. Furthermore, user log analysis indicates thatImpRec can support CIA in industry, and developers acknowledge the value of ImpRec in interviews. In conclusion, our findings showthe potential of reusing traceability associated with developers’ past activities in an RSSE

sted, utgiver, år, opplag, sider
IEEE, 2017. Vol. 43, nr 7, s. 675-700
Emneord [en]
Context, Industries, Software engineering, Unified modeling language, Automation, Software systems
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-13688DOI: 10.1109/TSE.2016.2620458ISI: 000405704400005OAI: oai:DiVA.org:bth-13688DiVA, id: diva2:1061125
Prosjekter
EASEORION
Forskningsfinansiär
Knowledge Foundation, 20140218Tilgjengelig fra: 2016-12-31 Laget: 2016-12-31 Sist oppdatert: 2018-05-22bibliografisk kontrollert

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Forlagets fullteksthttp://ieeexplore.ieee.org/document/7637029/

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