A neural network approach for predicting software development faults

Accurately predicting the number of faults in program modules is a major problem in the quality control of a large scale software system. In this paper, the use of the neural networks as a tool for predicting the number of faults in programs is explored. Software complexity metrics have been shown to be closely related to the distribution of faults in program modules. The objective in the construction of models of software quality is to use measures that may be obtained relatively early in the software development life cycle to provide reasonable initial estimates of quality of an evolving software system. Measures of software quality and software complexity to be used in this modeling process exhibit systematic departures of normality assumptions of regression modeling. This paper introduces a new approach for static reliability modeling and compares its performance in the modeling of software reliability from software complexity in terms of the predictive quality and the quality of fit with more traditional regression modeling techniques. The neural networks did produce models with better quality of fit and predictive quality when applied to one data set obtained from a large commercial system.<
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