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Automatic creation of acceptance tests by extracting conditionals from requirements: NLP approach and case study
Netlight Consulting GmbH, DEU.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0003-3995-6125
University of Cologne, DEU.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0003-0619-6027
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2023 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 197, article id 111549Article in journal (Refereed) Published
Abstract [en]

Acceptance testing is crucial to determine whether a system fulfills end-user requirements. However, the creation of acceptance tests is a laborious task entailing two major challenges: (1) practitioners need to determine the right set of test cases that fully covers a requirement, and (2) they need to create test cases manually due to insufficient tool support. Existing approaches for automatically deriving test cases require semi-formal or even formal notations of requirements, though unrestricted natural language is prevalent in practice. In this paper, we present our tool-supported approach CiRA (Conditionals in Requirements Artifacts) capable of creating the minimal set of required test cases from conditional statements in informal requirements. We demonstrate the feasibility of CiRA in a case study with three industry partners. In our study, out of 578 manually created test cases, 71.8% can be generated automatically. Additionally, CiRA discovered 80 relevant test cases that were missed in manual test case design. CiRA is publicly available at www.cira.bth.se/demo/. © 2022

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 197, article id 111549
Keywords [en]
Acceptance testing, Automatic test case creation, Causality extraction, Natural language processing, Requirements engineering, Natural language processing systems, Software testing, Automatic creations, Case-studies, Language processing, Natural languages, Requirement engineering, Test case, Acceptance tests
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-24047DOI: 10.1016/j.jss.2022.111549ISI: 000926985500008Scopus ID: 2-s2.0-85142730522OAI: oai:DiVA.org:bth-24047DiVA, id: diva2:1718228
Part of project
SERT- Software Engineering ReThought, Knowledge Foundation
Funder
Knowledge Foundation, 20180010Available from: 2022-12-12 Created: 2022-12-12 Last updated: 2023-03-09Bibliographically approved

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Frattini, JulianMendez, DanielUnterkalmsteiner, Michael

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