Score-Based Automatic Detection and Resolution of Syntactic Ambiguity in Natural Language Requirements

The quality of a delivered product relies heavily upon the quality of its requirements. Across many disciplines and domains, system and software requirements are mostly specified in natural language (NL). However, natural language is inherently ambiguous and inconsistent. Such intrinsic challenges can lead to misinterpretations and errors that propagate to the subsequent phases of the system development. Pattern-based natural language processing (NLP) techniques have been proposed to detect the ambiguity in requirements specifications. However, such approaches typically address specific cases or patterns and lack the versatility essential to detecting different cases and forms of ambiguity. In this paper, we propose an efficient and versatile automatic syntactic ambiguity detection technique for NL requirements. The proposed technique relies on filtering the possible scored interpretations of a given sentence obtained via Stanford CoreNLP library. In addition, it provides feedback to the user with the possible correct interpretations to resolve the ambiguity. Our approach incorporates four filtering pipelines on the input NL-requirements working in conjunction with the CoreNLP library to provide the most likely possible correct interpretations of a requirement. We evaluated our approach on a suite of datasets of 126 requirements and achieved 65% precision and 99% recall on average.