Provenance-enhanced Algorithmic Debugging
Localizing defects in a faulty software is a notoriously difficult activity. Researchers proposed several techniques to help developers to locate defects. One of these techniques is Algorithmic Debugging, which consists on executing the defective program, building an execution tree with the subcomputations, asking questions to the developer about the correctness of some specific subcomputations, and pruning the search space according to the answers to those questions. However, depending on the complexity of the program, the number of questions can be high, increasing the duration of the debug session. In this work we propose DebugProv, an algorithmic debugging approach for Python programs that enhances the execution tree with provenance to reduce the number of necessary questions to locate the defect, and, consequently, reduce the duration of debug sessions. We evaluated our technique over different programs and found that it was able to reduce the number of questions in 25.26%, on average.
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