Advanced Machine Learning Software Cost Prediction Model using AdaBoost and COCOMO Cost Parameters

Abstract

Playing a pivotal role in software development, the Constructive Cost Model (COCOMO) offers a systematic and structured approach to cost estimation. It stands as a widely utilized model, aiding project managers in estimating the required effort, time, and cost for software development projects. COCOMO takes into consideration diverse factors, including the project's size, complexity, and the experience of the development team. The utilization of COCOMO empowers software development teams to make informed decisions related to resource allocation, project scheduling, and budgeting. Its application extends to managing expectations, enhancing project planning, and mitigating the risk of cost overruns. The integration of machine learning assumes a critical role in advancing cost estimation within the realm of software development, specifically through COCOMO. Through the utilization of machine learning algorithms, COCOMO gains the capability to analyze and interpret extensive datasets, taking into account numerous complex factors that influence project costs. This work aims to propose a model for software cost estimation using an advanced machine learning technique, i.e. Adaptive boosting, which has improved accuracy, reduced overfitting, effectiveness with imbalanced data, and good generalization capabilities. The proposed work may contribute to the success of software projects, providing a reliable and comprehensive framework for cost estimation.