Priorização de requisitos de software: uma análise sobre as principais técnicas adotadas na indústria e na academia

Abstract

Requirements prioritization is a critical activity in software engineering that focuses on determining the relative importance of requirements, guiding implementation decisions to maximize value delivery. It supports effective communication between clients and project teams and contributes to proper requirements management throughout the development life cycle. Correctly planning the order in which requirements will be implemented is essential for the success of a software project, prioritizing those that deliver the greatest value to the customer and the business. However, requirements prioritization is a complex and iterative task that demands consideration of multiple criteria to support decision-making. At this stage, it is common to conduct negotiations among stakeholders, assess costs and associated risks, and adopt strategies for conflict resolution, aiming to meet the expectations of all parties involved in the project. Over the years, different techniques have been developed to assist development teams in this task, ranging from well-established traditional methods to advanced computational approaches based on Artificial Intelligence and Machine Learning. This dissertation aims to provide a comprehensive understanding of the main requirements prioritization practices used in both academia and industry, identifying the challenges faced, highlighting trends, and discussing opportunities for improvement. To achieve these objectives, the research was conducted in three complementary stages: (i) an updated Systematic Literature Review (SLR) on software requirements prioritization techniques, covering studies published between 2021 and 2025; (ii) a survey with software development professionals from diverse profiles and organizational contexts; and (iii) an integrated analysis through triangulation of the results obtained in the previous stages, comparing convergences, divergences, and gaps between industrial practice and academic research. The study revealed a misalignment between academia and industry in requirements prioritization. While the literature presents advanced AI/ML-based approaches, professionals prefer simpler and more practical methods. This contrast highlights the need to turn academic advances into scalable and accessible solutions, fostering better alignment between theory and practice. Future research opportunities include simplifying complex techniques and examining human and organizational factors that remain central to successful prioritization.