Fuzzy-based assessment of stakeholder feedback in software requirements

Requirements engineering is essential for aligning software systems with stakeholder needs, yet incorporating stakeholder feedback into existing software requirements remains challenging due to ambiguity, inconsistency, and limited validation mechanisms. This paper proposes an AI-based evaluation approach for assessing the quality of software requirements and the correctness of feedback-driven modifications. The method combines natural language processing, large language models, and a hierarchical fuzzy inference system to support systematic evaluation. A multi-label text classification model is trained to identify common requirement defects, including ambiguity, subjectivity, vagueness, non-verifiability, and negativity. These indicators are aggregated using a fuzzy inference hierarchy to compute requirement quality scores and evaluate the preservation and correctness of changes introduced by stakeholder feedback. The approach is implemented as a web-based software system and evaluated against multiple AI-based methods and human assessments using defined quality criteria and performance metrics. Results indicate that the proposed hybrid method provides consistent and interpretable evaluations and can support more structured assessment of stakeholder feedback implementation in requirements engineering. First published online 3 June 2026