Prioritizing Dynamic Program Slices Based on Probabilistic Inference
Xia Jia-bin · Computer and Modernization · 2013
In order to increase the productivity of the debugging process,this paper proposes a novel strategy for prioritizing dynamic program slices by automatically calculating the probability of correctness of each statement based on the dynamic slices.First,a runtime dependence graph of the observed program outcome is extracted.The next step is the transformation from the dependence network to a Bayesian network.Finally,run a probabilistic inference for the likelihood of correctness of the execution instances and estimates the corresponding correctness of the static statements.Programmers can administer a guided bug locating process using this ranking of correctness belief.The results shows that on average our tool rank the faults to 20.2% of the dynamic slices.