Enhancing analogy-based software cost estimation using Grey Wolf Optimization algorithm.

Abstract

Accurate software cost estimation (SCE) is a critical factor in the successful delivery of software projects, as highlighted by industry statistics indicating that only some of the projects comply with the predicted budget. Among the software estimation methods, analogy-based estimation (ABE) is one of the most popular ones. Although this method has been customized in recent years with the help of optimization algorithms to achieve better results, the use of more powerful optimization algorithms can be effective in achieving better results in software size estimation. This study presents an innovative approach to SCE that integrates the grey wolf optimization (GWO) algorithm to enhance the precision of ABE. The GWO algorithm, inspired by the hunting behavior and social hierarchy of grey wolves, is mathematically modeled and incorporated into the ABE approach. The key focus of this research is the optimization of the similarity function, a crucial component of the ABE, using both Euclidean and Manhattan distance measures. The article addresses the challenges in selecting an optimal similarity function and emphasizes the importance of proper feature weighting to improve estimation accuracy. The proposed GWO-based ABE method is rigorously evaluated on multiple software project datasets using cross-validation techniques. The performance of the GWO-based ABE is compared to other evolutionary algorithms based on widely accepted evaluation metrics. The results confirm that the integration of the GWO algorithm into ABE enhances estimation accuracy and model robustness. By optimizing feature weights in the similarity function, GWO-ABE effectively addresses key limitations of traditional analogy-based methods. The proposed approach demonstrates superior performance across multiple datasets, particularly under the Euclidean distance function, making it a reliable solution for software project cost estimation. Experimental evaluations show that GWO-ABE achieves notable improvements in key performance metrics, leading to reduced mean magnitude of relative error (MMRE), median magnitude of relative error (MdMRE), and higher percentage of prediction (PRED) compared to other ABE-customized methods. These findings highlight the role of metaheuristic optimization in improving software estimation techniques, contributing to more precise and efficient project planning and management.