Predicting project delivery rates using the Naive-Bayes classifier

The importance of accurate estimation of software development effort is well recognized in software engineering. In recent years, machine learning approaches have been studied as possible alternatives to more traditional software cost estimation methods. The objective of this paper is to investigate the utility of the machine learning algorithm known as the Naive-Bayes classifier for estimating software project effort. We present empirical experiments with the Benchmark 6 data set from the International Software Benchmarking Standards Group to estimate project delivery rates and compare the performance of the Naive-Bayes approach to two other machine learning methods--model trees and neural networks. A project delivery rate is defined as the number of effort hours per function point. The approach described is general and can be used to analyse not only software development data but also data on software maintenance and other types of software engineering. The paper demonstrates that the Naive-Bayes classifier has a potential to be used as an alternative machine learning tool for software development effort estimation.

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