Proceedings of the 2008 ACM symposium on Applied computing
Information and Software Technology
A shift-invariant morphological system for software development cost estimation
Expert Systems with Applications: An International Journal
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
Hybrid morphological methodology for software development cost estimation
Expert Systems with Applications: An International Journal
Software effort prediction using fuzzy clustering and functional link artificial neural networks
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
A grammatical evolution approach for software effort estimation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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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.