J Syst Soft 23(2):111–122īland JM, Altman DG (1995) Multiple significance tests: the bonferroni method. In: Fundamental approaches to software engineering, Springer, pp 59–73īasili V, Briand L, Melo W (1993) Object-oriented metrics that predict maintainability. In: IEEE International conference on Multitopic, INMIC’08, pp 349–356īacchelli A, DAmbros, M, Lanza M (2010) Are popular classes more defect prone?. We also perform Kruskal–Wallis test and Dunn’s multiple comparison test to compare the relative performance of the considered fault prediction techniques.Īfzal W, Torkar R, Feldt R (2008) prediction of fault count data using genetic programming. The results of the investigation are evaluated using average absolute error, average relative error, measure of completeness, and prediction at level l measures. The experimental investigation is carried out for eighteen software project datasets collected from the PROMISE data repository. In this paper, we present an experimental study to evaluate and compare the capability of six fault prediction techniques such as genetic programming, multilayer perceptron, linear regression, decision tree regression, zero-inflated Poisson regression, and negative binomial regression for the prediction of number of faults. The techniques such as Poisson regression, negative binomial regression, genetic programming, decision tree regression, and multilayer perceptron can be used for the prediction of the number of faults. Most of the earlier works on software fault prediction have used classification techniques for classifying software modules into faulty or non-faulty categories.

Such an approach may help in more focused software testing process and may enhance the reliability of the software system. During the software development process, prediction of the number of faults in software modules can be more helpful instead of predicting the modules being faulty or non-faulty.

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