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Empirical evaluations of regression test selection techniques: a systematic review
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- Engström, Emelie (författare)
- Lund University,Lunds universitet,Institutionen för datavetenskap,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Computer Science,Departments at LTH,Faculty of Engineering, LTH
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- Skoglund, Mats (författare)
- Lund University,Lunds universitet,Institutionen för datavetenskap,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Computer Science,Departments at LTH,Faculty of Engineering, LTH
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- Runeson, Per (författare)
- Lund University,Lunds universitet,Institutionen för datavetenskap,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Computer Science,Departments at LTH,Faculty of Engineering, LTH
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(creator_code:org_t)
- 2008-10-09
- Engelska 10 s.
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Ingår i: Proceedings of the Second ACM-IEEE international symposium on Empirical Software Engineering and Measurements. - New York, NY, USA : ACM. - 9781595939715 ; , s. 22-31
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Abstract
Ämnesord
Stäng
- Regression testing is the verification that previously functioning software remains after a change. In this paper we report on a systematic review of empirical evaluations of regression test selection techniques, published in major software engineering journals and conferences. Out of 2 923 papers analyzed in this systematic review, we identified 28 papers reporting on empirical comparative evaluations of regression test selection techniques. They report on 38 unique studies (23 experiments and 15 case studies), and in total 32 different techniques for regression test selection are evaluated. Our study concludes that no clear picture of the evaluated techniques can be provided based on existing empirical evidence, except for a small group of related techniques. Instead, we identified a need for more and better empirical studies were concepts are evaluated rather than small variations. It is also necessary to carefully consider the context in which studies are undertaken.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)