Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2017

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 behaviors. 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 feedbacks 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 bug-free 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 user study involving 16 participants on 3 debugging tasks. The results show that, compared to the participants using the baseline tool, Whyline, the ones using Microbat can spend on average 55.8% less time to locate the bugs.

Keywords

Approximation, Debugging, Feedback, Path Pattern, Slicing

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ICSE '17: Proceedings of the 39th IEEE/ACM International Conference on Software Engineering: Buenos Aires, Argentina, July 20-28

First Page

393

Last Page

403

ISBN

9781538638682

Identifier

10.1109/ICSE.2017.43

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICSE.2017.43

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