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Causality in requirements artifacts: prevalence, detection, and impact
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0003-3995-6125
Qualicen GmbH, GER.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0003-0619-6027
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0003-4118-0952
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2023 (English)In: Requirements Engineering, ISSN 0947-3602, E-ISSN 1432-010X, Vol. 28, no 1, p. 49-74Article in journal (Refereed) Published
Abstract [en]

Causal relations in natural language (NL) requirements convey strong, semantic information. Automatically extracting such causal information enables multiple use cases, such as test case generation, but it also requires to reliably detect causal relations in the first place. Currently, this is still a cumbersome task as causality in NL requirements is still barely understood and, thus, barely detectable. In our empirically informed research, we aim at better understanding the notion of causality and supporting the automatic extraction of causal relations in NL requirements. In a first case study, we investigate 14.983 sentences from 53 requirements documents to understand the extent and form in which causality occurs. Second, we present and evaluate a tool-supported approach, called CiRA, for causality detection. We conclude with a second case study where we demonstrate the applicability of our tool and investigate the impact of causality on NL requirements. The first case study shows that causality constitutes around 28 % of all NL requirements sentences. We then demonstrate that our detection tool achieves a macro-F 1 score of 82 % on real-world data and that it outperforms related approaches with an average gain of 11.06 % in macro-Recall and 11.43 % in macro-Precision. Finally, our second case study corroborates the positive correlations of causality with features of NL requirements. The results strengthen our confidence in the eligibility of causal relations for downstream reuse, while our tool and publicly available data constitute a first step in the ongoing endeavors of utilizing causality in RE and beyond. © 2022, The Author(s).

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2023. Vol. 28, no 1, p. 49-74
Keywords [en]
Causality, Multi-case study, Natural language processing, Requirements engineering, Semantics, Automatic extraction, Case-studies, Causal relations, Multiple use-cases, Natural language requirements, Requirement engineering, Requirements document, Semantics Information, Test case generation, Natural language processing systems
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-22673DOI: 10.1007/s00766-022-00371-xISI: 000753242500002Scopus ID: 2-s2.0-85124567603OAI: oai:DiVA.org:bth-22673DiVA, id: diva2:1640668
Part of project
SERT- Software Engineering ReThought, Knowledge Foundation
Note

open access

Available from: 2022-02-25 Created: 2022-02-25 Last updated: 2023-06-19Bibliographically approved
In thesis
1. Towards good-enough Requirements Engineering: a theoretical Foundation for Requirements Quality
Open this publication in new window or tab >>Towards good-enough Requirements Engineering: a theoretical Foundation for Requirements Quality
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Context: Requirements Engineering (RE) research has established a common agreement on the impact that the quality of requirements has on subsequent software development activities and artifacts. Furthermore, empirical investigations suppose that RE quality defects tend to scale in cost for remediation when left unattended. This motivates the need for requirements quality assurance.

Problem: This need has been met with requirements quality research, which abounds with publications proposing writing rules and guidelines that are meant to ensure requirements of high quality. However, recent studies have questioned the rigor and relevance of these publications, which would undermine the practical applicability of requirements quality research: requirements quality is a means to an end and serves a specific purpose (i.e., minimizing the emitted risk on downstream activities), but when this purpose is not met due to lack of a rigor and practical relevance, the approach to researching requirements quality needs to be rethought.

Aim: The notion of good-enough requirements engineering constitutes a context-sensitive, activity-based perspective on requirements quality. In this thesis, we aim at both (1) understanding and (2) exploring possibilities of operationalizing this notion.

Methods: We employ a mixed-methods approach to achieve our aim. We use theory adoption in order to provide a theoretical foundation for requirements quality research, conduct a survey to understand the level of theory adherence in the requirements quality literature, and perform subject-based classification to generate an overview of theory-related elements proposed in literature. 

Results: Through theory adoption we derive a harmonized, activity-based requirements quality theory that frames requirements quality according to its impact on subsequent activities and hence ensures its relevance. The subsequent survey confirms that there is a lack of rigor and relevance in previous requirements quality publications, which likely explains the lack of adoption of the research in practice. The overview of quality factors in a subject-based classification is a first step to centralize requirements quality research for visibility and effective reuse.

Conclusion: The notion of good-enough requirements engineering has the potential to re-focus requirements quality research on a more profound notion of rigor and relevance. In this thesis, we report on a first requirements quality theory. Through adherence to this requirements quality theory and contribution to the central repository of subject-based classification, the operationalization of the concept of good-enough requirements engineering can effectively support predicting the impact that requirements quality has on subsequent software development activities in the future.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2023. p. 184
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 1
Keywords
Requirements Engineering, Requirements Quality, Theory Development
National Category
Software Engineering
Research subject
Software Engineering
Identifiers
urn:nbn:se:bth-23948 (URN)978-91-7295-447-2 (ISBN)
Presentation
2023-01-13, J1630 och Zoom, Campus Karlskrona, 09:00 (English)
Opponent
Supervisors
Available from: 2022-11-21 Created: 2022-11-18 Last updated: 2022-12-08Bibliographically approved

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Frattini, JulianMendez, DanielUnterkalmsteiner, MichaelWnuk, Krzysztof

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