A Semiautomated Approach for Detecting Ambiguities in Software Requirements Using SpanBERT and Named Entity Recognition

Ambiguous user requirements present a challenge in software requirement engineering. A manual approach to handling ambiguity is time-consuming. Software requirements are essential inputs to software development processes, including architecture and design, implementation, and testing. Requirement ambiguities lead to project cost overruns, delays in project delivery, and poor software product quality. Timely identification and correction of ambiguity can result in better software systems that meet product objectives and satisfy the needs of all stakeholders. This study explores various natural language processing techniques and SpanBERT (a variant of BERT). This research proposes a semiautomated approach for detecting anaphoric, coordination, and missing condition ambiguities in functional requirements. The proposed approach is validated on a new, original dataset containing 425 functional requirements from 16 domains. The ambiguities identified through our approach are compared with those detected manually and by ChatGPT. Our approach outperforms ChatGPT in detecting ambiguities. The proposed approach will aid project managers and requirement engineers in identifying ambiguities in requirement specifications, thereby helping to reduce cost overruns and delays in the software development process caused by requirement ambiguities.