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In the realm of software development, the clarity, completeness, andcomprehensiveness of requirements significantly impact the success of softwaresystems. The Software Requirement Specification (SRS) document, a cornerstoneof the software development life cycle, delineates both functional andnonfunctional requirements, playing a pivotal role in ensuring the quality andtimely delivery of software projects. However, the inherent natural languagerepresentation of these requirements poses challenges, leading to potentialmisinterpretations and conflicts. This study addresses the need for conflictidentification within requirements by delving into their semantic compositionsand contextual meanings. Our research introduces an automated supervisedconflict detection method known as the Supervised Semantic Similarity-basedConflict Detection Algorithm (S3CDA). This algorithm comprises two phases:identifying conflict candidates through textual similarity and employingsemantic analysis to filter these conflicts. The similarity-based conflictdetection involves leveraging sentence embeddings and cosine similaritymeasures to identify pertinent candidate requirements. Additionally, we presentan unsupervised conflict detection algorithm, UnSupCDA, combining keycomponents of S3CDA, tailored for unlabeled software requirements.Generalizability of our methods is tested across five SRS documents fromdiverse domains. Our experimental results demonstrate the efficacy of theproposed conflict detection strategy, achieving high accuracy in automatedconflict identification.

Supervised Semantic Similarity-based Conflict Detection Algorithm: S3CDA