  In the realm of software development, the clarity, completeness, and
comprehensiveness of requirements significantly impact the success of software
systems. The Software Requirement Specification (SRS) document, a cornerstone
of the software development life cycle, delineates both functional and
nonfunctional requirements, playing a pivotal role in ensuring the quality and
timely delivery of software projects. However, the inherent natural language
representation of these requirements poses challenges, leading to potential
misinterpretations and conflicts. This study addresses the need for conflict
identification within requirements by delving into their semantic compositions
and contextual meanings. Our research introduces an automated supervised
conflict detection method known as the Supervised Semantic Similarity-based
Conflict Detection Algorithm (S3CDA). This algorithm comprises two phases:
identifying conflict candidates through textual similarity and employing
semantic analysis to filter these conflicts. The similarity-based conflict
detection involves leveraging sentence embeddings and cosine similarity
measures to identify pertinent candidate requirements. Additionally, we present
an unsupervised conflict detection algorithm, UnSupCDA, combining key
components of S3CDA, tailored for unlabeled software requirements.
Generalizability of our methods is tested across five SRS documents from
diverse domains. Our experimental results demonstrate the efficacy of the
proposed conflict detection strategy, achieving high accuracy in automated
conflict identification.
