Identifying Conflicting Requirements in Problem‐Oriented Requirements Engineering: A Case Study Based Approach Using Enhanced Pairwise Comparison Method (E‐ <scp>PCM</scp> )

ABSTRACT Purpose In the fields of requirements engineering and problem‐oriented requirements analysis, successful conflict identification and resolution are essential to the development and deployment of software systems. The purpose of this paper is to identify conflicting requirements using the Enhanced Pairwise Comparison Method (E‐PCM) applied to a real‐world mobile phone case study within the Jackson problem frame framework. Method This paper enhances the traditional PCM by incorporating a full Natural Language Processing (NLP) pipeline comprising entity extraction, parsing rules, and conflict identification using the spaCy library. The methodology further introduces a modified contribution table to represent identified conflicts, and employs empirical evaluation with a structured conflict scaling system (0–5) in place of fuzzy logic for attribute weighting. Results The results indicate potential conflicts between requirements, categorized as no, minimal, moderate, substantial, high, and extreme conflict, each assigned a corresponding level from 0 to 5. The E‐PCM model achieves a precision of 1.0 (100%) across all nine requirements, with recall ranging from 0.5 to 1.0 and an F1‐Score ranging from 0.66 to 1.0. The overall accuracy of E‐PCM is 87%, outperforming the cluster‐based approach (85.71%). Inter‐rater agreement among 78 undergraduate participants was substantial ( k = 0.769), and Fisher's Exact Test validated the NLP‐based conflict predictions ( p ≤ 0.05 for 13 out of 15 sub‐RQs). These findings provide stakeholders with actionable insights to inform decision‐making and requirement prioritization. Conclusion This paper addresses a significant gap in requirements engineering by introducing the first NLP‐augmented pairwise comparison method natively aligned with the Jackson problem frame. Unlike existing approaches, E‐PCM combines entity extraction, semantic parsing rules, and empirical conflict scaling to provide a domain‐independent, scalable, and data‐driven conflict identification framework acknowledged both in the literature and by practitioners.