Software Effort Estimation using Machine Learning Techniques with Robust Confidence Intervals

Authors:
Petronio L. Braga;Adriano L. I. Oliveira;Silvio R. L. Meira
Affiliations:
Pernambuco State University;Pernambuco State University;Federal University of Pernambuco
Venue:
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Year:
2007

Citing 0
Cited 4

Applying support vector regression for web effort estimation using a cross-company dataset

ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Using Support Vector Regression for Web Development Effort Estimation

IWSM '09 /Mensura '09 Proceedings of the International Conferences on Software Process and Product Measurement
How effective is Tabu search to configure support vector regression for effort estimation?

Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Investigating the use of Support Vector Regression for web effort estimation

Empirical Software Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

The precision and reliability of the estimation of the effort of software projects is very important for the competitiveness of software companies. Good estimates play a very important role in the management of software projects. Most methods proposed for effort estimation, including methods based on machine learning, provide only an estimate of the effort for a novel project. In this paper we introduce a method based on machine learning which gives the estimation of the effort together with a confidence interval for it. In our method, we propose to employ robust confidence intervals, which do not depend on the form of probability distribution of the errors in the training set. We report on a number of experiments using two datasets aimed to compare machine learning techniques for software effort estimation and to show that robust confidence intervals can be successfully built.