Reference : A Machine Learning-Based Approach for Demarcating Requirements in Textual Specifications
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/39889
A Machine Learning-Based Approach for Demarcating Requirements in Textual Specifications
English
Abualhaija, Sallam mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Arora, Chetan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Sabetzadeh, Mehrdad mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > ; University of Ottawa > School of Engineering and Computer Science]
Vaz, Eduardo [QRA Corp]
2019
27th IEEE International Requirements Engineering Conference (RE'19)
Yes
27th IEEE International Requirements Engineering Conference (RE'19)
September 21-27, 2019
[en] Textual Requirements ; Requirements Identification and Classification ; Machine Learning ; Natural Language Processing
[en] 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 30 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.
http://hdl.handle.net/10993/39889
H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems
FnR ; FNR12632261 > Mehrdad Sabetzadeh > EQUACS > Early QUality Assurance of Critical Systems > 01/01/2019 > 31/12/2021 > 2018

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