Reference : A Machine Learning-Based Approach for Demarcating Requirements in Textual Specifications

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/39889

Title :	A Machine Learning-Based Approach for Demarcating Requirements in Textual Specifications
Language :	English
Author, co-author :	Abualhaija, Sallam [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Arora, Chetan [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Sabetzadeh, Mehrdad [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Briand, Lionel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > ; University of Ottawa > School of Engineering and Computer Science]
	Vaz, Eduardo [QRA Corp]
Publication date :	2019
Main document title :	27th IEEE International Requirements Engineering Conference (RE'19)
Peer reviewed :	Yes
Event name :	27th IEEE International Requirements Engineering Conference (RE'19)
Event date :	September 21-27, 2019
Keywords :	[en] Textual Requirements ; Requirements Identification and Classification ; Machine Learning ; Natural Language Processing
Abstract :	[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.
Permalink :	http://hdl.handle.net/10993/39889
DOI :	10.1109/RE.2019.00017
Framework Programme / European Project :	H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems
FnR project :	FnR ; FNR12632261 > Mehrdad Sabetzadeh > EQUACS > Early QUality Assurance of Critical Systems > 01/01/2019 > 31/12/2021 > 2018

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