From Complaints to Requirements: Using NLP and QFD to Derive Use Cases for University Academic Management System - Santana Neto, José Pedro de; Santos, Cleiton Almeida dos; Rezine, Jaison Pisa; Alves, Marco Antonio Zanata; Dominico, Simone; Peres, Leticia Mara; Todt, Eduardo (Anais do Simpósio Brasileiro de Qualidade de Software (SBQS); 2025: Anais do XXIV Simpósio Brasileiro de Qualidade de Software, 2025)
This study introduces a hybrid methodology combining Natural Language Processing (NLP) and Quality Function Deployment (QFD) to enhance the quality and responsiveness of institutional academic systems. The approach systematically transforms unstructured user complaints into functional software requirements, thereby improving documentation and maintenance in legacy systems. By leveraging NLP to extract and prioritize user needs based on dissatisfaction severity and frequency, and translating these into technical requirements via QFD, the method creates a scalable, usercentered, and traceable requirements engineering process, ideal for legacy systems with limited documentation. Analyzing 4,357 service tickets, our results identify critical improvement areas, including authentication, enrollment, and bureaucratic workflows. Although steps like technical mapping and modeling remain manual, the methodology boosts prioritization, transparency, and alignment with essential software quality attributes. While applied to this specific context, the framework shows potential for transferability to other institutional or commercial sectors. These findings demonstrate the value of integrating AI with software engineering principles to modernize complex institutional platforms and increase user satisfaction.