Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation

The average software company spends a huge amount of its revenue on Research and Development (R&D) for how to deliver software on time. Accurate software effort estimation is critical for successful project planning, resource allocation, and on-time delivery within budget for sustainable software development. However, both overestimation and underestimation can pose significant challenges, highlighting the need for continuous improvement in estimation techniques. This study reviews recent machine learning approaches employed to enhance the accuracy of software effort estimation (SEE), focusing on research published between 2020 and 2023. The literature review employed a systematic approach to identify relevant research on machine learning techniques for SEE. Additionally, comparative experiments were conducted using five commonly employed Machine Learning (ML) methods: K-Nearest Neighbor, Support Vector Machine, Random Forest, Logistic Regression, and LASSO Regression. The performance of these techniques was evaluated using five widely adopted accuracy metrics: Mean Squared Error (MSE), Mean Magnitude of Relative Error (MMRE), R-squared, Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The evaluation was carried out on seven benchmark datasets: Albrecht, Desharnais, China, Kemerer, Mayazaki94, Maxwell, and COCOMO, which are publicly available and extensively used in SEE research. By carefully reviewing study quality, analyzing results across the literature, and rigorously evaluating experimental outcomes, clear conclusions were drawn about the most promising techniques for achieving state-of-the-art accuracy in estimating software effort. This study makes three key contributions to the field: firstly, it furnishes a thorough overview of recent machine learning research in software effort estimation (SEE); secondly, it provides data-driven guidance for researchers and practitioners to select optimal methods for accurate effort estimation; and thirdly, it demonstrates the performance of publicly available datasets through experimental analysis. Enhanced estimation supports the development of better predictive models for software project time, cost, and staffing needs. The findings aim to guide future research directions and tool development toward the most accurate machine learning approaches for modelling software development effort, costs, and delivery schedules, ultimately contributing to more efficient and cost-effective software projects.