Chicago, IL, USA USA
Sept. 24, 2012 to Sept. 28, 2012
Bashar Nuseibeh , Department of Computing, The Open University, UK
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.
rule-based approach, Uncertainty, natural language requirements, speculative requirements, uncertainty cues, machine learning, uncertainty scopes
Bashar Nuseibeh, "Speculative requirements: Automatic detection of uncertainty in natural language requirements", RE, 2012, 2013 21st IEEE International Requirements Engineering Conference (RE), 2013 21st IEEE International Requirements Engineering Conference (RE) 2012, pp. 11-20, doi:10.1109/RE.2012.6345795