Automatic Detection of Causality in Requirement Artifacts: The CiRA Approach

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Automatic Detection of Causality in Requirement Artifacts : The CiRA Approach

Fischbach, Jannik (författare): Qualicen GmbH, DEU

Frattini, Julian, 1995- (författare): Blekinge Tekniska Högskola,Institutionen för programvaruteknik

Spaans, Arjen (författare): Qualicen GmbH, DEU

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Kummeth, Maximilian (författare): Qualicen GmbH, DEU

Vogelsang, Andreas (författare): University of Cologne, DEU

Mendez, Daniel (författare): Blekinge Tekniska Högskola,Institutionen för programvaruteknik

Unterkalmsteiner, Michael (författare): Blekinge Tekniska Högskola,Institutionen för programvaruteknik

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2021-04-02
2021
Engelska.
Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030731274 ; , s. 19-36

Relaterad länk:: https://bth.diva-por... (primary) (Raw object); visa fler...; http://arxiv.org/pdf...; https://urn.kb.se/re...; https://doi.org/10.1...; visa färre...

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Abstract Ämnesord

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[Context & motivation:] System behavior is often expressed by causal relations in requirements (e.g., If event 1, then event 2). Automatically extracting this embedded causal knowledge supports not only reasoning about requirements dependencies, but also various automated engineering tasks such as seamless derivation of test cases. However, causality extraction from natural language (NL) is still an open research challenge as existing approaches fail to extract causality with reasonable performance. [Question/problem:] We understand causality extraction from requirements as a two-step problem: First, we need to detect if requirements have causal properties or not. Second, we need to understand and extract their causal relations. At present, though, we lack knowledge about the form and complexity of causality in requirements, which is necessary to develop a suitable approach addressing these two problems. [Principal ideas/results:] We conduct an exploratory case study with 14,983 sentences from 53 requirements documents originating from 18 different domains and shed light on the form and complexity of causality in requirements. Based on our findings, we develop a tool-supported approach for causality detection (CiRA, standing for Causality in Requirement Artifacts). This constitutes a first step towards causality extraction from NL requirements. [Contribution:] We report on a case study and the resulting tool-supported approach for causality detection in requirements. Our case study corroborates, among other things, that causality is, in fact, a widely used linguistic pattern to describe system behavior, as about a third of the analyzed sentences are causal. We further demonstrate that our tool CiRA achieves a macro-F 1 score of 82% on real word data and that it outperforms related approaches with an average gain of 11.06% in macro-Recall and 11.43% in macro-Precision. Finally, we disclose our open data sets as well as our tool to foster the discourse on the automatic detection of causality in the RE community. © 2021, Springer Nature Switzerland AG.

Ämnesord

NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)

Nyckelord

Case study
Causality
Natural Language Processing
Requirements engineering
Computer software selection and evaluation
Extraction
Macros
Automatic Detection
Different domains
Exploratory case studies
Knowledge supports
Linguistic patterns
Requirements dependencies
Requirements document
Research challenges
Open Data

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