Predicate Proximity in Failure: An MLP based Fault Localization approach

Fault localization (FL) is a time consuming and tedious task during program debugging. Most of the existing FL methods use statement coverage information to prioritize the statements based upon a computed suspiciousness score. We use predicate level execution trace to train a multilayer perceptron neural network model for effective fault localization. After prioritizing the fault at predicate level, we search the statements bounded by the predicates. Also, dynamic slicing is used to reduce the search space. We have experimentally studied the performance of our approach over Siemens suite and Space program and found that it is performing on an average 39.12% more effectively than DStar, a state-of-the-art bug localization technique.