Systematic Mapping of AI-Based Approaches for Requirements Prioritization

Abstract

Context and Motivation: Requirements prioritization (RP) is a main concern of requirements engineering (RE). Traditional prioritization techniques, while effective, often involve manual effort and are time-consuming. In recent years, thanks to the advances in AI-based techniques and algorithms, several promising alternatives have emerged to optimize this process.

Question: The main goal of this work is to review the current state of requirement prioritization, focusing on AI-based techniques and a classification scheme to provide a comprehensive overview. Additionally, we examine the criteria utilized by these AI-based techniques, as well as the datasets and evaluation metrics employed. For this purpose, we conducted a systematic mapping study (SMS) of studies published between 2011 and 2023.

Results: Our analysis reveals a diverse range of AI-based techniques in use, with fuzzy logic being the most commonly applied. Moreover, most studies continue to depend on stakeholder input as a key criterion, limiting the potential for full automation of the prioritization process. Finally, there appears to be no standardized evaluation metric or dataset across the reviewed papers, focusing on the need for standardized approaches across studies.

Contribution: This work provides a systematic categorization of current AI-based techniques used for automating RP. Additionally, it updates and expands existing reviews, offering a valuable resource for practitioners and nonspecialists.