Software Cost Estimation Using Machine Learning Techniques

Abstract

Accurate software cost estimation (SCE) is paramount for effective project management, enabling stakeholders to allocate resources efficiently, manage timelines, and mitigate financial risks. Traditional parametric models, such as the Constructive Cost Model (COCOMO), often struggle to capture the intricate, nonlinear relationships and complex interactions inherent in modern software development projects. This research addresses this limitation by applying a suite of advanced machine learning (ML) techniques—including Linear Regression, Decision Trees, Random Forest, Support Vector Regression (SVR), and Artificial Neural Networks (ANN)—to the widely recognized NASA93 dataset.

 The study meticulously details the entire workflow, encompassing data preprocessing, comprehensive feature analysis, robust model evaluation, and insightful visualization of results. Our findings unequivocally demonstrate that ensemble methods, particularly Random Forest, and neural network architectures significantly outperform conventional estimation approaches in terms of accuracy, reliability, and predictive power. This paper not Preprocessing, feature analysis, model evaluation, workflow modeling, and visualization are fully detailed. Results demonstrate that Random Forest and ANN significantly outperform traditional methods in accuracy and reliability. This paper not only highlights the superior performance of ML models but also provides an in-depth analysis of their underlying mechanisms, contributing to a clearer understanding of their applicability and impact in the domain of software cost estimation.