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Speculative requirements: automatic detection of uncertainty in natural language requirements

Yang, Hui; De Roeck, Anne; Gervasi, Vincenzo; Willis, Alistair and Nuseibeh, Bashar (2012). Speculative requirements: automatic detection of uncertainty in natural language requirements. In: The 20th IEEE International Requirements Engineering Conference, 24-28 September 2012, Chicago, ILL, USA, IEEE, pp. 11–20.

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DOI (Digital Object Identifier) Link: http://doi.org/10.1109/RE.2012.6345795
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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 lan-guage, 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 lin-guistic 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.

Item Type: Conference Item
Copyright Holders: 2012 IEEE
ISSN: 1090-750X
Project Funding Details:
Funded Project NameProject IDFunding Body
MaTREx project EP/F068859/1EPSRC (Engineering and Physical Sciences Research Council)
Not Set10/CE/I1855Science Foundation Ireland
Not SetNot SetERC
Keywords: uncertainty; natural language requirements; speculative requirements; uncertainty cues; machine learning; uncertainty scopes; rule-based approach
Academic Unit/Department: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Centre for Policing Research and Learning (CPRL)
Item ID: 34479
Depositing User: Hui Yang
Date Deposited: 09 Oct 2012 14:55
Last Modified: 04 Oct 2016 11:20
URI: http://oro.open.ac.uk/id/eprint/34479
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