Reference : Leveraging Natural-language Requirements for Deriving Better Acceptance Criteria from...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/43900

Title :	Leveraging Natural-language Requirements for Deriving Better Acceptance Criteria from Models
Language :	English
Author, co-author :	Veizaga Campero, Alvaro Mario [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Alferez, Mauricio [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Torre, Damiano [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Sabetzadeh, Mehrdad [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > ; University of Ottawa, Canada > School of Electrical Engineering and Computer Science]
	Briand, Lionel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > ; University of Ottawa, Canada > School of Electrical Engineering and Computer Science]
	Pitskhelauri, Elene [Clearstream Services SA, Luxembourg]
Publication date :	Oct-2020
Main document title :	Proceedings of 23rd ACM / IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS)
Pages :	218-228
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	23rd ACM / IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS)
Event date :	19-10-2020 to 23-10-2020
Event place (city) :	Montreal
Event country :	Canada
Keywords :	[en] Requirements Validation and Verification ; Acceptance Testing ; Acceptance Criteria ; UML ; Controlled Natural Language ; Gherkin
Abstract :	[en] In many software and systems development projects, analysts specify requirements using a combination of modeling and natural language (NL). In such situations, systematic acceptance testing poses a challenge because defining the acceptance criteria (AC) to be met by the system under test has to account not only for the information in the (requirements) model but also that in the NL requirements. In other words, neither models nor NL requirements per se provide a complete picture of the information content relevant to AC. Our work in this paper is prompted by the observation that a reconciliation of the information content in NL requirements and models is necessary for obtaining precise AC. We perform such reconciliation by devising an approach that automatically extracts AC-related information from NL requirements and helps modelers enrich their model with the extracted information. An existing AC derivation technique is then applied to the model that has now been enriched by the information extracted from NL requirements. Using a real case study from the financial domain, we evaluate the usefulness of the AC-related model enrichments recommended by our approach. Our evaluation results are very promising: Over our case study system, a group of five domain experts found 89% of the recommended enrichments relevant to AC and yet absent from the original model (precision of 89%). Furthermore, the experts could not pinpoint any additional information in the NL requirements which was relevant to AC but which had not already been brought to their attention by our approach (recall of 100%)
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Funders :	Clearstream Services SA ; Fonds National de la Recherche - FnR ; NSERC of Canada under the Discovery, Discovery Accelerator and CRC programs
Name of the research project :	Improved Model-based Requirements for Financial Applications (IMoReF)
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/43900
DOI :	10.1145/3365438.3410953
FnR project :	FnR ; FNR13234469 > Lionel Briand > IMoReF > Improved Model-based Requirements for Financial Applications > 01/01/2019 > 31/12/2021 > 2018

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