AI-Enabled Hybrid Fuzzy Framework for Software Development Optimization: Integrating WPM, TOPSIS, Deep Learning, and Particle Swarm Optimization in Legacy ERP Environments
DOI:
https://doi.org/10.15662/IJARCST.2021.0406008Keywords:
Legacy ERP Systems, Software Development Optimization, AI-Enabled Framework, Hybrid Fuzzy Logic, Weighted Product Method (WPM), TOPSIS, Particle Swarm Optimization (PSO), Deep Learning, Predictive Maintenance, Multi-Criteria Decision-Making, Enterprise Software ModernizationAbstract
Legacy ERP systems pose significant challenges for modern software development due to complex architectures, rigid processes, and high maintenance costs. This research proposes an AI-enabled hybrid fuzzy framework to optimize software development in legacy ERP environments. The framework integrates Weighted Product Method (WPM) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for multi-criteria decision-making, Particle Swarm Optimization (PSO) for adaptive parameter tuning, and deep learning models for predictive analysis of development outcomes.
The hybrid fuzzy component effectively handles uncertainty and vagueness in ERP system requirements, enabling more informed decision-making during software maintenance, module integration, and system upgrades. WPM and TOPSIS are employed to systematically rank development strategies based on performance, reliability, and cost-efficiency, while PSO optimizes resource allocation and task scheduling in real time. Deep learning supports anomaly detection, effort estimation, and predictive maintenance, enhancing automation and reducing human intervention.
Experimental evaluation demonstrates significant improvements in development efficiency, deployment reliability, and maintenance cost reduction, validating the framework’s suitability for modernizing legacy ERP systems while leveraging AI-driven optimization techniques. This study contributes a unified approach that combines AI, fuzzy logic, multi-criteria decision-making, and swarm intelligence for sustainable software development in enterprise environments.
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