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Which requirements artifact quality defects are automatically detectable?: A case study
Technical University Munich, GER.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. (Software Engineering Research Lab Sweden)ORCID iD: 0000-0003-4118-0952
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2017 (English)In: Proceedings - 2017 IEEE 25th International Requirements Engineering Conference Workshops, REW 2017, IEEE, 2017, p. 400-406, article id 8054884Conference paper, Published paper (Refereed)
Abstract [en]

[Context:] The quality of requirements engineeringartifacts, e.g. requirements specifications, is acknowledged tobe an important success factor for projects. Therefore, manycompanies spend significant amounts of money to control thequality of their RE artifacts. To reduce spending and improvethe RE artifact quality, methods were proposed that combinemanual quality control, i.e. reviews, with automated approaches.[Problem:] So far, we have seen various approaches to auto-matically detect certain aspects in RE artifacts. However, westill lack an overview what can and cannot be automaticallydetected. [Approach:] Starting from an industry guideline forRE artifacts, we classify 166 existing rules for RE artifacts alongvarious categories to discuss the share and the characteristics ofthose rules that can be automated. For those rules, that cannotbe automated, we discuss the main reasons. [Contribution:] Weestimate that 53% of the 166 rules can be checked automaticallyeither perfectly or with a good heuristic. Most rules need onlysimple techniques for checking. The main reason why some rulesresist automation is due to imprecise definition. [Impact:] Bygiving first estimates and analyses of automatically detectable andnot automatically detectable rule violations, we aim to provide anoverview of the potential of automated methods in requirementsquality control.

Place, publisher, year, edition, pages
IEEE, 2017. p. 400-406, article id 8054884
Keyword [en]
Artifact quality, Automated methods, Requirement engineering
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-15453DOI: 10.1109/REW.2017.18ISI: 000427148000063ISBN: 9781538634882 (print)OAI: oai:DiVA.org:bth-15453DiVA, id: diva2:1155358
Conference
Fourth International Workshop on Artificial Intelligence for Requirements Engineering (AIRE'17), Lisboa
Available from: 2017-11-07 Created: 2017-11-07 Last updated: 2018-04-04Bibliographically approved

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CiteExportLink to record
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