Bridging Precision and Complexity: A Novel Machine Learning Approach for Ambiguity Detection in Software Requirements

Ambiguity in software requirements is a significant challenge as it often leads to misunderstandings, implementation errors, and costly project delays. This research proposes a hybrid framework that combines rule-based techniques with machine learning to identify ambiguity in software requirements with precision and efficiency. The framework begins with a rule-based model that systematically detects ambiguities using a carefully prepared list of ambiguous phrases. The analysis utilizes a dataset of 1,553 software requirements drawn from diverse project domains. To capture more intricate ambiguities that traditional rule-based systems might miss, the framework integrates TF-IDF vectorization and a Random Forest classifier, enhancing the precision and coverage of classification. In addition, clustering analysis identifies patterns to provide deeper insights into ambiguous requirements, while sentiment analysis explores the relationship between ambiguity and the emotional tone of requirements. Together, these analyses offer a broader understanding of ambiguity trends and stakeholder perceptions. The framework's performance is validated using standard evaluation metrics, achieving an accuracy of 97%, precision of 97%, recall of 89%, and an F1-score of 92%, significantly surpassing traditional rule-based methodologies. This research advances automatic ambiguity detection by delivering a flexible and interpretable solution. The proposed approach enhances the clarity and quality of software requirements, strengthening requirements engineering practices and supporting more effective software development.