CHEAP

Software cost estimation provides the foundation for planning the job scheduling and budget, the accuracy of an estimate can deter- mine if the project is able to meet its objectives. However, effective cost estimation requires a thorough understanding of the project scope, deliverables and the effort required to develop the project, so it is very complex. For this reason, our main goal is to develop a machine learning model to estimate software project development effort in order to help project manager in their job. In particular, we intend to use a specific Ensembling method, that is Stacking, to improve the generalization and the accuracy of the predictions and mitigate the small size of the datasets related to the issue at hand. Moreover, the designed model should be able to adapt to new data, in order to provide project managers with an accurate and reliable cost prediction system that help them efficiently planning development activities. A comprehensive analysis of the existing cost estimation models is finally carried out with the purpose of assessing how competitive the proposed model could be compared to state-of-art technologies

Project Report

Authors

• @Pasquale Somma
• @Daniele Donia
• @Antonio La Marca