Software Effort Estimation using parameter tuned Models

TL;DR

This paper explores the use of hyperparameter-tuned regression models to improve the accuracy of early-stage software effort estimation, addressing the challenge of imprecise predictions in complex and evolving software projects.

Contribution

It introduces a novel analysis of nine regression models with hyperparameter tuning for more accurate software effort estimation.

Findings

Hyperparameter tuning improves model accuracy.

Regression models outperform traditional estimation methods.

Enhanced models reduce estimation errors significantly.

Abstract

Software estimation is one of the most important activities in the software project. The software effort estimation is required in the early stages of software life cycle. Project Failure is the major problem undergoing nowadays as seen by software project managers. The imprecision of the estimation is the reason for this problem. Assize of software size grows, it also makes a system complex, thus difficult to accurately predict the cost of software development process. The greatest pitfall of the software industry was the fast-changing nature of software development which has made it difficult to develop parametric models that yield high accuracy for software development in all domains. We need the development of useful models that accurately predict the cost of developing a software product. This study presents the novel analysis of various regression models with hyperparameter tuning to get the effective model. Nine different regression techniques are considered for model development