Software Testing Cost Reduction with Genetic Algorithms and Neural Networks
Alison Watkins, Sergio V. Davalos · Digital Commons - University of South Florida (University of South Florida) · 2014
Highly complex and interconnected systems may suffer from intermittent or transient software failures. These are particularly difficult to diagnose without large quantities of test cases. This research focuses on a hybrid method for generating test cases. A genetic algorithm is first used to automatically generating large numbers of test cases to form a comprehensive test suite. These test suites are then used to train a neural network for regression testing and test suite augmentation. The results indicate that the genetic algorithm can produce a balanced test suite that, when combined with a neural network, can reduce the costs of software testing by reducing system run-time and human interaction.