NDLI: Speculative requirements: Automatic detection of uncertainty in natural language requirements

Content Provider	IEEE Xplore Digital Library
Author	Hui Yang De Roeck, A. Gervasi, V. Willis, A. Nuseibeh, B.
Copyright Year	2012
Description	Author affiliation: Department of Computer Science, University of Pisa, Italy (Gervasi, V.) \|\| Department of Computing, The Open University, UK (Hui Yang; De Roeck, A.; Willis, A.; Nuseibeh, B.)
Abstract	Stakeholders frequently use speculative language when they need to convey their requirements with some degree of uncertainty. Due to the intrinsic vagueness of speculative language, speculative requirements risk being misunderstood, and related uncertainty overlooked, and may benefit from careful treatment in the requirements engineering process. In this paper, we present a linguistically-oriented approach to automatic detection of uncertainty in natural language (NL) requirements. Our approach comprises two stages. First we identify speculative sentences by applying a machine learning algorithm called Conditional Random Fields (CRFs) to identify uncertainty cues. The algorithm exploits a rich set of lexical and syntactic features extracted from requirements sentences. Second, we try to determine the scope of uncertainty. We use a rule-based approach that draws on a set of hand-crafted linguistic heuristics to determine the uncertainty scope with the help of dependency structures present in the sentence parse tree. We report on a series of experiments we conducted to evaluate the performance and usefulness of our system.
Starting Page	11
Ending Page	20
File Size	241656
Page Count	10
File Format	PDF
ISBN	9781467327831
ISSN	1090750X
e-ISBN	9781467327855
e-ISBN	9781467327848
DOI	10.1109/RE.2012.6345795
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Pragmatics Syntactics Natural languages Feature extraction Context Machine learning rule-based approach natural language requirements speculative requirements uncertainty cues machine learning uncertainty scopes
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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