Feedback-based debugging
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,...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2017
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/4709 https://ink.library.smu.edu.sg/context/sis_research/article/5712/viewcontent/feedback_based_debugging_av.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-5712 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-57122020-03-26T08:02:39Z Feedback-based debugging LIN, Yun SUN, Jun XUE, Yinxing LIU, Yang DONG, Jin Song 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. 2017-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4709 info:doi/10.1109/ICSE.2017.43 https://ink.library.smu.edu.sg/context/sis_research/article/5712/viewcontent/feedback_based_debugging_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Approximation Debugging Feedback Path Pattern Slicing Software Engineering |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Approximation Debugging Feedback Path Pattern Slicing Software Engineering |
spellingShingle |
Approximation Debugging Feedback Path Pattern Slicing Software Engineering LIN, Yun SUN, Jun XUE, Yinxing LIU, Yang DONG, Jin Song Feedback-based debugging |
description |
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. |
format |
text |
author |
LIN, Yun SUN, Jun XUE, Yinxing LIU, Yang DONG, Jin Song |
author_facet |
LIN, Yun SUN, Jun XUE, Yinxing LIU, Yang DONG, Jin Song |
author_sort |
LIN, Yun |
title |
Feedback-based debugging |
title_short |
Feedback-based debugging |
title_full |
Feedback-based debugging |
title_fullStr |
Feedback-based debugging |
title_full_unstemmed |
Feedback-based debugging |
title_sort |
feedback-based debugging |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2017 |
url |
https://ink.library.smu.edu.sg/sis_research/4709 https://ink.library.smu.edu.sg/context/sis_research/article/5712/viewcontent/feedback_based_debugging_av.pdf |
_version_ |
1770574985902948352 |