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

The average software company spends a huge amount of its revenue on 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 pose significant challenges in software development, necessitating continuous improvement in estimation techniques. This study reviews recent machine learning approaches exploited to enhance software effort estimation (SEE) accuracy, focusing on research published between 2020 and 2023. The literature review employed an approach to identify pertinent research on machine learning techniques for software estimation efforts. Additionally, comparative experiments were conducted employing five commonly used ML methods: K-Nearest Neighbor, Support Vector Machine, Random Forest, Logistic Regression, and…