Feedback-based debugging

abstract：

software debugging has long been regarded as a time and effort consuming task. In the process of debugging, developers usually need to manually inspect many program steps to see whether they deviate from their intended behavior. Given that intended behaviors usually exist nowhere but in human mind, the automation of debugging turns out to be extremely hard, if not impossible.

In this work, we propose a feedback-based debugging approach, which (1) builds on light-weight human feedbacks on a buggy program and (2) regards the feedback as partial program specification to infer suspicious steps of the buggy execution, Given a buggy program, we record its execution trace and allow developers to provide light-weight feedback on trace steps. Based on the feedbacks, we recommend suspicious steps on the trace. Moreover, our approach can further learn and approximate bugfree paths, which helps reduce required feedbacks to expedite the debugging process. We conduct an experiment to evaluate our approach with simulated feedbacks on 3409 mutated bugs across 3 open source projects. The results show that our feedback-based approach can detect 92.8% of the bugs and 65%of the detected bugs require less than 20 feedbacks. in addition, we implement our proof-of-concept tool, Microbat, and conduct a