Evolutionary neural networks: a robust approach to software reliability problems

In this empirical study, from a large data set of software metrics for program modules, thirty distinct partitions into training and validation sets are automatically generated with approximately equal distributions of fault prone and not fault prone modules. Thirty classification models are built for each of the two approaches considered-discriminant analysis and the evolutionary neural network (ENN) approach-and their performances on corresponding data sets are compared. The lower error proportions for ENNs on fault prone, not fault prone, and overall classification were found to be statistically significant. The robustness of ENNs follows from their superior performance on the range of data configurations used. It is suggested that ENNs can be effective in other software reliability problem domains, where they have been largely ignored.

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