Automatic Extraction of Cause-Effect-Relations from Requirements Artifacts
Background: The detection and extraction of causality from natural language sentences have shown great potential in various fields of application. The field of requirements engineering is eligible for multiple reasons: (1) requirements artifacts are primarily written in natural language, (2) causal...
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| Vydáno v: | 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) s. 561 - 572 |
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| Jazyk: | angličtina |
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ACM
01.09.2020
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| ISSN: | 2643-1572 |
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| Abstract | Background: The detection and extraction of causality from natural language sentences have shown great potential in various fields of application. The field of requirements engineering is eligible for multiple reasons: (1) requirements artifacts are primarily written in natural language, (2) causal sentences convey essential context about the subject of requirements, and (3) extracted and formalized causality relations are usable for a (semi-)automatic translation into further artifacts, such as test cases. Objective: We aim at understanding the value of interactive causality extraction based on syntactic criteria for the context of requirements engineering. Method: We developed a prototype of a system for automatic causality extraction and evaluate it by applying it to a set of publicly available requirements artifacts, determining whether the automatic extraction reduces the manual effort of requirements formalization. Result: During the evaluation we analyzed 4457 natural language sentences from 18 requirements documents, 558 of which were causal (12.52%). The best evaluation of a requirements document provided an automatic extraction of 48.57% cause-effect graphs on average, which demonstrates the feasibility of the approach. Limitation: The feasibility of the approach has been proven in theory but lacks exploration of being scaled up for practical use. Evaluating the applicability of the automatic causality extraction for a requirements engineer is left for future research. Conclusion: A syntactic approach for causality extraction is viable for the context of requirements engineering and can aid a pipeline towards an automatic generation of further artifacts from requirements artifacts. |
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| AbstractList | Background: The detection and extraction of causality from natural language sentences have shown great potential in various fields of application. The field of requirements engineering is eligible for multiple reasons: (1) requirements artifacts are primarily written in natural language, (2) causal sentences convey essential context about the subject of requirements, and (3) extracted and formalized causality relations are usable for a (semi-)automatic translation into further artifacts, such as test cases. Objective: We aim at understanding the value of interactive causality extraction based on syntactic criteria for the context of requirements engineering. Method: We developed a prototype of a system for automatic causality extraction and evaluate it by applying it to a set of publicly available requirements artifacts, determining whether the automatic extraction reduces the manual effort of requirements formalization. Result: During the evaluation we analyzed 4457 natural language sentences from 18 requirements documents, 558 of which were causal (12.52%). The best evaluation of a requirements document provided an automatic extraction of 48.57% cause-effect graphs on average, which demonstrates the feasibility of the approach. Limitation: The feasibility of the approach has been proven in theory but lacks exploration of being scaled up for practical use. Evaluating the applicability of the automatic causality extraction for a requirements engineer is left for future research. Conclusion: A syntactic approach for causality extraction is viable for the context of requirements engineering and can aid a pipeline towards an automatic generation of further artifacts from requirements artifacts. |
| Author | Mendez, Daniel Frattini, Julian Unterkalmsteiner, Michael Junker, Maximilian |
| Author_xml | – sequence: 1 givenname: Julian surname: Frattini fullname: Frattini, Julian email: julian.frattini@bth.se organization: Blekinge Institute of Technology,Karlskrona,Sweden – sequence: 2 givenname: Maximilian surname: Junker fullname: Junker, Maximilian email: maximilian.junker@qualicen.de organization: Qualicen GmbH,Munich,Germany – sequence: 3 givenname: Michael surname: Unterkalmsteiner fullname: Unterkalmsteiner, Michael email: michael.unterkalmsteiner@bth.se organization: Blekinge Institute of Technology,Karlskrona,Sweden – sequence: 4 givenname: Daniel surname: Mendez fullname: Mendez, Daniel email: daniel.mendez@bth.se organization: Blekinge Institute of Technology and fortiss GmbH,Karlskrona,Sweden |
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| Snippet | Background: The detection and extraction of causality from natural language sentences have shown great potential in various fields of application. The field of... |
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| SubjectTerms | causality extraction Manuals natural language processing Natural languages pattern matching Pipelines Prototypes requirements artifacts Requirements engineering Software engineering Syntactics |
| Title | Automatic Extraction of Cause-Effect-Relations from Requirements Artifacts |
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