SwePub
Sök i LIBRIS databas

  Utökad sökning

WFRF:(Mendez Daniel)
 

Sökning: WFRF:(Mendez Daniel) > Causality in requir...

Causality in requirements artifacts : prevalence, detection, and impact

Frattini, Julian, 1995- (författare)
Blekinge Tekniska Högskola,Institutionen för programvaruteknik
Fischbach, Jannik (författare)
Qualicen GmbH, GER
Mendez, Daniel (författare)
Blekinge Tekniska Högskola,Institutionen för programvaruteknik
visa fler...
Unterkalmsteiner, Michael (författare)
Blekinge Tekniska Högskola,Institutionen för programvaruteknik
Vogelsang, Andreas (författare)
University of Cologne, GER
Wnuk, Krzysztof, 1982- (författare)
Blekinge Tekniska Högskola,Institutionen för programvaruteknik
visa färre...
 (creator_code:org_t)
2022-02-09
2023
Engelska.
Ingår i: Requirements Engineering. - : Springer Science+Business Media B.V.. - 0947-3602 .- 1432-010X. ; 28:1, s. 49-74
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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).

Ämnesord

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

Nyckelord

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

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy