Machine Learning Models for Software Cost Estimation
TL;DR
Results show that machine learning methods can be used to predict software cost with a high accuracy rate, and demonstrate that ensemble and neural network-based models generally outperform traditional statistical methods in terms of prediction accuracy and adaptability to non-linear relationships within project data.
Abstract
Abstract Software cost estimation is a critical task in software projects development. It assists project managers and software engineers to plan and manage their resources. However, developing an accurate cost estimation model for a software project is a challenging process. This paper builds a software cost estimation model using machine learning approach. Different machine learning algorithms are applied to two public datasets to predict the software cost in the early stages. Results show that machine learning methods can be used to predict software cost with a high accuracy rate. Accurate software cost estimation remains one of the most critical challenges in software project management, directly influencing budgeting, resource allocation, scheduling, and overall project success. Traditional estimation techniques—such as expert judgment, algorithmic models (e.g., COCOMO), and function point analysis—often struggle with the increasing complexity, scale, and variability of modern software projects, leading to inaccurate predictions and cost overruns. This project explores the application of machine learning (ML) models to improve the accuracy and reliability of software cost estimation. Various ML techniques, including Linear Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), and Artificial Neural Networks (ANN), are investigated and compared using historical software project datasets (such as COCOMO, Desharnais, or NASA datasets). Key project attributes—such as lines of code, function points, team experience, development environment, and project complexity—are used as input features to predict effort and cost outcomes. Data preprocessing techniques, including normalization, feature selection, and handling of missing values, are applied to enhance model performance. The performance of each model is evaluated using metrics such as Mean Magnitude of Relative Error (MMRE), Root Mean Square Error (RMSE), and Prediction Accuracy (PRED), enabling a comparative analysis to identify the most effective approach. Experimental results demonstrate that ensemble and neural network-based models generally outperform traditional statistical methods in terms of prediction accuracy and adaptability to non-linear relationships within project data. This project highlights the potential of machine learning as a robust, data-driven alternative to conventional cost estimation methods, offering improved decision-making support for project managers and stakeholders. Future work may focus on integrating deep learning architectures, real-time data streams, and hybrid models to further enhance estimation precision across diverse software development environments. Keywords: Software Cost Estimation, Machine Learning, Effort Prediction, COCOMO Model, Regression Analysis, Neural Networks, Random Forest, Support Vector Machine, Predictive Modeling, Software Project Management, Data Preprocessing, Feature Selection