Speculative Requirements: Automatic Detection of Uncertainty

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 detec-tion of uncertainty in natural language (NL) requirements. Our approach comprises two stages. First we identify speculative sen-tences by applying a machine learning algorithm called Condi-tional 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 deter-mine 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 ex-periments we conducted to evaluate the performance and useful-ness of our system