Software Effort Estimation Using Machine Learning Techniques with Robust Confidence Intervals

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 o...

Full description

Saved in:
Bibliographic Details
Main Authors: Braga, P.L., Oliveira, A.L.I., Meira, S.R.L.
Format: Conference Proceeding
Language:English
Subjects:
Bagging
Linear regression
Machine learning
NASA
Neural networks
Project management
Robustness
Software tools
State estimation
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 for the effort estimation can be successfully built.
ISSN:1082-3409
2375-0197
DOI:10.1109/ICTAI.2007.172