A review of optimization techniques for cost estimation in software development

Abstract

In software development, cost estimation remains a significant challenge. Despite numerous research efforts, identifying an optimal technique applicable to all situations has proven difficult. The increasing adoption of agile development methods has further complicated accurate cost estimation. Recently, the application of optimization techniques, particularly metaheuristic optimization algorithms, has increased to enhance estimation accuracy and performance. However, there is a lack of systematic literature reviews exploring these optimization techniques in software cost estimation (SCE). This study aims to fill this gap by employing a systematic literature review (SLR) method to select, filter, and analyze relevant literature from 2019 to 2024. The review included 41 journal articles and 11 conference proceedings to evaluate the current development status of optimization methodologies in SCE. The study identified 20 key optimization algorithms with the top 6 most commonly used: Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), Gray Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Bat algorithm (BA). Their key contributions are parameter tuning, feature analysis, and model optimization. While optimization techniques hold promise, their application in cost estimation must carefully consider these challenges, such as premature convergence, sensitivity to initial parameters, etc. Findings also reveal significant limitations for Agile Software Cost Estimation (ASCE), such as the less applicability of traditional cost estimation techniques, lack of cost drivers for agile, more reliance on expertise and experience, and few public datasets. This research provides valuable insights for further exploration of optimization techniques in SCE.