Software reliability engineering: An evolutionary neural network approach

This thesis presents the results of an empirical investigation of the applicability of genetic algorithms to a real-world problem in software reliability--the fault-prone module identification problem. The solution developed is an effective hybrid of genetic algorithms and neural networks. This approach (ENNs) was found to be superior, in terms of time, effort, and confidence in the optimality of results, to the common practice of searching manually for the best-performing net. Comparisons were made to discriminant analysis. On fault-prone, not-fault-prone, and overall classification, the lower error proportions for ENNs were found to be statistically significant. The robustness of ENNs follows from their superior performance over many data configurations. Given these encouraging results, it is suggested that ENNs have potential value in other software reliability problem domains, where genetic algorithms have been largely ignored. For future research, several plans are outlined for enhancing ENNs with respect to accuracy and applicability.