Yang, Hui; De Roeck, Anne; Gervasi, Vincenzo; Willis, Alistair and Nuseibeh, Bashar
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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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.
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