Bayes‐TDG: effective test data generation using Bayesian belief network: toward failure‐detection effectiveness and maximum coverage

Abstract

This study presents a novel test data generation method called Bayes-TDG. It is based on principles of Bayesian networks and provides the possibility of making inference from probabilistic data in the model to increase the prime path-coverage ratio for a given programme under test (PUT). In this regard, a new programme structure-based probabilistic network, TDG-NET, is proposed that is capable of modelling the conditional dependencies among the programme basic blocks (BBs) on one hand and conditional dependencies of the transitions between its BBs and input parameters on the other hand. To achieve failure-detection effectiveness, the authors propose a path selection strategy that works based on the predicted outcome of generated test cases. So, they 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.