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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00005031naa a2200601 4500
001oai:DiVA.org:bth-21703
003SwePub
008210618s2021 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:bth-217032 URI
0247 a https://doi.org/10.1007/978-3-030-73128-1_22 DOI
040 a (SwePub)bth
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a kon2 swepub-publicationtype
1001 a Fischbach, Janniku Qualicen GmbH, DEU4 aut
2451 0a Automatic Detection of Causality in Requirement Artifacts :b The CiRA Approach
264 c 2021-04-02
264 1a Cham :b Springer Science and Business Media Deutschland GmbH,c 2021
338 a electronic2 rdacarrier
520 a [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.
650 7a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe
650 7a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng
650 7a NATURVETENSKAPx Data- och informationsvetenskapx Programvaruteknik0 (SwePub)102052 hsv//swe
650 7a NATURAL SCIENCESx Computer and Information Sciencesx Software Engineering0 (SwePub)102052 hsv//eng
653 a Case study
653 a Causality
653 a Natural Language Processing
653 a Requirements engineering
653 a Computer software selection and evaluation
653 a Extraction
653 a Macros
653 a Automatic Detection
653 a Different domains
653 a Exploratory case studies
653 a Knowledge supports
653 a Linguistic patterns
653 a Requirements dependencies
653 a Requirements document
653 a Research challenges
653 a Open Data
7001 a Frattini, Julian,d 1995-u Blekinge Tekniska Högskola,Institutionen för programvaruteknik4 aut0 (Swepub:bth)juf
7001 a Spaans, Arjenu Qualicen GmbH, DEU4 aut
7001 a Kummeth, Maximilianu Qualicen GmbH, DEU4 aut
7001 a Vogelsang, Andreasu University of Cologne, DEU4 aut
7001 a Mendez, Danielu Blekinge Tekniska Högskola,Institutionen för programvaruteknik4 aut0 (Swepub:bth)dmz
7001 a Unterkalmsteiner, Michaelu Blekinge Tekniska Högskola,Institutionen för programvaruteknik4 aut0 (Swepub:bth)mun
7102 a Qualicen GmbH, DEUb Institutionen för programvaruteknik4 org
7730 t Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)d Cham : Springer Science and Business Media Deutschland GmbHg , s. 19-36q <19-36z 9783030731274
8564 u https://bth.diva-portal.org/smash/get/diva2:1568890/FULLTEXT01.pdfx primaryx Raw objecty fulltext:postprint
8564 u http://arxiv.org/pdf/2101.10766
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:bth-21703
8564 8u https://doi.org/10.1007/978-3-030-73128-1_2

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