Effective test data generation using probabilistic networks

This paper presents a novel test data generation method called Bayes-TDG. It is based on principles of Bayesian networks (BNs) and provides the possibility of making inference from probabilistic data in the model to increase the prime path coverage ratio (PPCR) for a given program under test (PUT). In this regard, a new program structure-based probabilistic network, TDG-NET, is proposed that is capable of modelling the conditional dependencies among the program basic blocks (BBs) in one hand and conditional dependencies of the transitions between its BBs and input parameters on the other hand. To achieve failure-detection effectiveness, we propose a path selection strategy that works based on the predicted outcome of generated test cases. So, we mitigate the need for a human oracle, and the generated test suite could be directly used in fault localisation. Several experiments are conducted to evaluate the performance of Bayes-TDG. The results reveal that the method is promising and the generated test suite could be quite effective.