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SERP-test: a taxonomy for supporting industry-academia communication
Lund University, SWE.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
Lund University, SWE.
2017 (English)In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, Vol. 25, no 4, p. 1269-1305Article in journal (Refereed) Published
Abstract [en]

This paper presents the construction and evaluation of SERP-test, a taxonomy aimed to improve communication between researchers and practitioners in the area of software testing. SERP-test can be utilized for direct communication in industry academia collaborations. It may also facilitate indirect communication between practitioners adopting software engineering research and researchers who are striving for industry relevance. SERP-test was constructed through a systematic and goal-oriented approach which included literature reviews and interviews with practitioners and researchers. SERP-test was evaluated through an online survey and by utilizing it in an industry–academia collaboration project. SERP-test comprises four facets along which both research contributions and practical challenges may be classified: Intervention, Scope, Effect target and Context constraints. This paper explains the available categories for each of these facets (i.e., their definitions and rationales) and presents examples of categorized entities. Several tasks may benefit from SERP-test, such as formulating research goals from a problem perspective, describing practical challenges in a researchable fashion, analyzing primary studies in a literature review, or identifying relevant points of comparison and generalization of research.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2017. Vol. 25, no 4, p. 1269-1305
Keywords [en]
Classification (of information); Software engineering; Taxonomies; Testing, Context; Industry relevance; Intervention; Methodology; Scope, Software testing
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-13103DOI: 10.1007/s11219-016-9322-xISI: 000415973100007Scopus ID: 2-s2.0-84976367380OAI: oai:DiVA.org:bth-13103DiVA, id: diva2:1023820
Available from: 2016-10-04 Created: 2016-10-03 Last updated: 2018-01-16Bibliographically approved

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Petersen, KaiAli, Nauman

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
  • Other style
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
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  • Other locale
More languages
Output format
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