Spectrum-Based Fault Localization Framework to Support Fault Understanding

Yong WANG; Zhiqiu HUANG; Yong LI; RongCun WANG; Qiao YU

doi:10.1587/transinf.2018EDL8233

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Spectrum-Based Fault Localization Framework to Support Fault Understanding

Yong WANG, Zhiqiu HUANG, Yong LI, RongCun WANG, Qiao YU

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Summary :

A spectrum-based fault localization technique (SBFL), which identifies fault location(s) in a buggy program by comparing the execution statistics of the program spectra of passed executions and failed executions, is a popular automatic debugging technique. However, the usefulness of SBFL is mainly affected by the following two factors: accuracy and fault understanding in reality. To solve this issue, we propose a SBFL framework to support fault understanding. In the framework, we firstly localize a suspicious fault module to start debugging and then generate a weighted fault propagation graph (WFPG) for the hypothesis fault module, which weights the suspiciousness for the nodes to further perform block-level fault localization. In order to evaluate the proposed framework, we conduct a controlled experiment to compare two different module-level SBFL approaches and validate the effectiveness of WFPG. According to our preliminary experiments, the results are promising.

Publication: IEICE TRANSACTIONS on Information Vol.E102-D No.4 pp.863-866

Publication Date: 2019/04/01

Publicized: 2019/01/15

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2018EDL8233

Type of Manuscript: LETTER

Category: Software Engineering

Authors

Yong WANG
  Anhui Polytechnic University,Nanjing University of Aeronautics and Astronautics,Ministry of Industry and Information Technology
Zhiqiu HUANG
  Nanjing University of Aeronautics and Astronautics,Ministry of Industry and Information Technology
Yong LI
  Nanjing University of Aeronautics and Astronautics
RongCun WANG
  China University of Mining and Technology
Qiao YU
  China University of Mining and Technology

Keyword

fault understanding, spectrum-based fault localization, method- and block-level debugging

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Yong WANG, Zhiqiu HUANG, Yong LI, RongCun WANG, Qiao YU, "Spectrum-Based Fault Localization Framework to Support Fault Understanding" in IEICE TRANSACTIONS on Information, vol. E102-D, no. 4, pp. 863-866, April 2019, doi: 10.1587/transinf.2018EDL8233.
Abstract: A spectrum-based fault localization technique (SBFL), which identifies fault location(s) in a buggy program by comparing the execution statistics of the program spectra of passed executions and failed executions, is a popular automatic debugging technique. However, the usefulness of SBFL is mainly affected by the following two factors: accuracy and fault understanding in reality. To solve this issue, we propose a SBFL framework to support fault understanding. In the framework, we firstly localize a suspicious fault module to start debugging and then generate a weighted fault propagation graph (WFPG) for the hypothesis fault module, which weights the suspiciousness for the nodes to further perform block-level fault localization. In order to evaluate the proposed framework, we conduct a controlled experiment to compare two different module-level SBFL approaches and validate the effectiveness of WFPG. According to our preliminary experiments, the results are promising.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8233/_p

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@ARTICLE{e102-d_4_863,
author={Yong WANG, Zhiqiu HUANG, Yong LI, RongCun WANG, Qiao YU, },
journal={IEICE TRANSACTIONS on Information},
title={Spectrum-Based Fault Localization Framework to Support Fault Understanding},
year={2019},
volume={E102-D},
number={4},
pages={863-866},
abstract={A spectrum-based fault localization technique (SBFL), which identifies fault location(s) in a buggy program by comparing the execution statistics of the program spectra of passed executions and failed executions, is a popular automatic debugging technique. However, the usefulness of SBFL is mainly affected by the following two factors: accuracy and fault understanding in reality. To solve this issue, we propose a SBFL framework to support fault understanding. In the framework, we firstly localize a suspicious fault module to start debugging and then generate a weighted fault propagation graph (WFPG) for the hypothesis fault module, which weights the suspiciousness for the nodes to further perform block-level fault localization. In order to evaluate the proposed framework, we conduct a controlled experiment to compare two different module-level SBFL approaches and validate the effectiveness of WFPG. According to our preliminary experiments, the results are promising.},
keywords={},
doi={10.1587/transinf.2018EDL8233},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - Spectrum-Based Fault Localization Framework to Support Fault Understanding
T2 - IEICE TRANSACTIONS on Information
SP - 863
EP - 866
AU - Yong WANG
AU - Zhiqiu HUANG
AU - Yong LI
AU - RongCun WANG
AU - Qiao YU
PY - 2019
DO - 10.1587/transinf.2018EDL8233
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2019
AB - A spectrum-based fault localization technique (SBFL), which identifies fault location(s) in a buggy program by comparing the execution statistics of the program spectra of passed executions and failed executions, is a popular automatic debugging technique. However, the usefulness of SBFL is mainly affected by the following two factors: accuracy and fault understanding in reality. To solve this issue, we propose a SBFL framework to support fault understanding. In the framework, we firstly localize a suspicious fault module to start debugging and then generate a weighted fault propagation graph (WFPG) for the hypothesis fault module, which weights the suspiciousness for the nodes to further perform block-level fault localization. In order to evaluate the proposed framework, we conduct a controlled experiment to compare two different module-level SBFL approaches and validate the effectiveness of WFPG. According to our preliminary experiments, the results are promising.
ER -