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Automated demarcation of requirements in textual specifications: a machine learning-based approach

Abualhaija, Sallam, Arora, Chetan, Sabetzadeh, Mehrdad, Briand, Lionel C. and Traynor, Michael 2020, Automated demarcation of requirements in textual specifications: a machine learning-based approach, Empirical Software Engineering, vol. 25, pp. 5454-5497, doi: 10.1007/s10664-020-09864-1.

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Title Automated demarcation of requirements in textual specifications: a machine learning-based approach
Author(s) Abualhaija, Sallam
Arora, ChetanORCID iD for Arora, Chetan orcid.org/0000-0003-1466-7386
Sabetzadeh, Mehrdad
Briand, Lionel C.
Traynor, Michael
Journal name Empirical Software Engineering
Volume number 25
Start page 5454
End page 5497
Total pages 44
Publisher Springer
Place of publication New York, N.Y.
Publication date 2020
ISSN 1382-3256
1573-7616
Keyword(s) Science & Technology
Technology
Computer Science, Software Engineering
Computer Science
Textual requirements
Requirements identification and classification
Machine learning
Natural language processing
CLASSIFICATION
requirements indentification and classification
Summary A simple but important task during the analysis of a textual requirements specification is to determine which statements in the specification represent requirements. In principle, by following suitable writing and markup conventions, one can provide an immediate and unequivocal demarcation of requirements at the time a specification is being developed. However, neither the presence nor a fully accurate enforcement of such conventions is guaranteed. The result is that, in many practical situations, analysts end up resorting to after-the-fact reviews for sifting requirements from other material in a requirements specification. This is both tedious and time-consuming. We propose an automated approach for demarcating requirements in free-form requirements specifications. The approach, which is based on machine learning, can be applied to a wide variety of specifications in different domains and with different writing styles. We train and evaluate our approach over an independently labeled dataset comprised of 33 industrial requirements specifications. Over this dataset, our approach yields an average precision of 81.2% and an average recall of 95.7%. Compared to simple baselines that demarcate requirements based on the presence of modal verbs and identifiers, our approach leads to an average gain of 16.4% in precision and 25.5% in recall. We collect and analyze expert feedback on the demarcations produced by our approach for industrial requirements specifications. The results indicate that experts find our approach useful and efficient in practice. We developed a prototype tool, named DemaRQ, in support of our approach. To facilitate replication, we make available to the research community this prototype tool alongside the non-proprietary portion of our training data.
Language eng
DOI 10.1007/s10664-020-09864-1
Indigenous content off
Field of Research 0803 Computer Software
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2020, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30143367

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.