| 2026 | |
| [1] | , "KPSO-Fuzzy: An AI-based Hybrid Approach for Software Requirements Prioritization", In e-Informatica Software Engineering Journal, vol. 20, no. 1, pp. 260108, 2026.
DOI: 10.37190/e-Inf260108. Download article (PDF)Get article BibTeX file |
Authors
Rahila Anwar, Muhammad Bilal Bashir
Abstract
Context: Software requirements outline customer expectations for their software and are critical for successful project outcomes. As software becomes increasingly complex due to its size and diverse features, it is vital to prioritize these requirements to utilize development resources effectively. To address this challenge, researchers are exploring new strategies and improved solutions using artificial intelligence (AI) tools. In our systematic literature review conducted in 2023, we found that existing requirements prioritization techniques predominantly rely on human input and have several limitations. These include inaccuracies, overlapping results, scalability issues, and excessive time consumption. These challenges can be mitigated by incorporating key features from AI-based software requirements prioritization techniques.
Objective: The primary goal of this study is to develop a Hybrid AI-based requirements prioritization technique named as “KPSO-Fuzzy that effectively balances user preferences and technical dependencies.
Method: We propose a Hybrid prioritization method called Hybrid KPSO-Fuzzy. First, we use K-Means clustering to group technically dependent functional requirements into three distinct clusters. In the next phase, we apply the Particle Swarm Optimization (PSO) algorithm along with Fuzzy Logic to prioritize each cluster simultaneously. Clusters that achieve higher accuracy will be selected to create the most effective prioritized lists of requirements. We conducted extensive experiments to validate our proposed approach, focusing on accuracy, scalability, and computation time.
Results: The comparative analysis shows that our proposed Hybrid KPSO-Fuzzy method outperforms PSO, Fuzzy Logic, Multi-objective Artificial Bee Colony optimization and Hybrid Genetic Algorithms in terms of accuracy, scalability, and efficiency.
Conclusions: Overall, this study clarifies the application domains for various AI-based techniques, enabling users to maximize their benefits.
Keywords
Artificial Intelligence; Clustering; Elbow Method; Fuzzy Logic; Genetic Algorithm: Hybrid Technique; Hybrid Genetic Algorithms; Machine Learning; Multi-objective Artificial Bee Colony Optimization; Particle Swarm Optimization; Requirement Engineering; Requirement Prioritization; Requirement Analysis.
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