Regression Based Software Project Effort Estimation with Reusability for Startups

Abstract

Prediction of effort for software development is the major task for the effective management of any software industry. At present, there are several estimation techniques and tools that are available for a software project. However, choosing the correct and efficient effort prediction of a particular software project is the most demanding task for a start ups. This study focuses on Multiple Linear regression for Software Effort Estimation technique which will provide higher classification accuracy of a software project and thus improves the prediction efficiently. The main aim of the technique is to help developers in startups to classify the project compared to existing software metrics and thereby increasing the software effort prediction rate.