Using genetic algorithms and decision tree induction to classify software failures

This paper describes two laboratory experiments designed to evaluate a failure-pursuit strategy for system level testing. In the first experiment, two GAs are used to automatically generate test suites that are rich in failure-causing test cases. Their performance is compared to random generation. The resulting test suites are then used to train a series of decision trees, producing rules for classifying other test cases. Finally, the performance of the classification rules is evaluated empirically. The results indicate that the combination of GA-based test case generation and decision tree induction can produce rules with high-predictive accuracy that can assist human testers in diagnosing the cause of system failures.