Theory and practice, do they match? A case with spectrum-based fault localization

Spectrum-based fault localization refers to the process of identifying program units that are buggy from two sets of execution traces: normal traces and faulty traces. These approaches use statistical formulas to measure the suspiciousness of program units based on the execution traces. There have b...

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Main Authors: LE, Tien-Duy B., THUNG, Ferdian, LO, David
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Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/sis_research/2020
https://ink.library.smu.edu.sg/context/sis_research/article/3019/viewcontent/4981a380.pdf
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spelling sg-smu-ink.sis_research-30192020-04-24T08:53:49Z Theory and practice, do they match? A case with spectrum-based fault localization LE, Tien-Duy B. THUNG, Ferdian LO, David Spectrum-based fault localization refers to the process of identifying program units that are buggy from two sets of execution traces: normal traces and faulty traces. These approaches use statistical formulas to measure the suspiciousness of program units based on the execution traces. There have been many spectrum-based fault localization approaches proposing various formulas in the literature. Two of the best performing and well-known ones are Tarantula and Ochiai. Recently, Xie et al. find that theoretically, under certain assumptions, two families of spectrum-based fault localization formulas outperform all other formulas including those of Tarantula and Ochiai. In this work, we empirically validate Xie et al.'s findings by comparing the performance of the theoretically best formulas against popular approaches on a dataset containing 199 buggy versions of 10 programs. Our empirical study finds that Ochiai and Tarantula statistically significantly outperforms 3 out of 5 theoretically best fault localization techniques. For the remaining two, Ochiai also outperforms them, albeit not statistically significantly. This happens because an assumption in Xie et al.'s work is not satisfied in many fault localization settings. 2013-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2020 info:doi/10.1109/ICSM.2013.52 https://ink.library.smu.edu.sg/context/sis_research/article/3019/viewcontent/4981a380.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 Empirical Study Program Spectra Fault Localization Theory Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Empirical Study
Program Spectra
Fault Localization
Theory
Software Engineering
spellingShingle Empirical Study
Program Spectra
Fault Localization
Theory
Software Engineering
LE, Tien-Duy B.
THUNG, Ferdian
LO, David
Theory and practice, do they match? A case with spectrum-based fault localization
description Spectrum-based fault localization refers to the process of identifying program units that are buggy from two sets of execution traces: normal traces and faulty traces. These approaches use statistical formulas to measure the suspiciousness of program units based on the execution traces. There have been many spectrum-based fault localization approaches proposing various formulas in the literature. Two of the best performing and well-known ones are Tarantula and Ochiai. Recently, Xie et al. find that theoretically, under certain assumptions, two families of spectrum-based fault localization formulas outperform all other formulas including those of Tarantula and Ochiai. In this work, we empirically validate Xie et al.'s findings by comparing the performance of the theoretically best formulas against popular approaches on a dataset containing 199 buggy versions of 10 programs. Our empirical study finds that Ochiai and Tarantula statistically significantly outperforms 3 out of 5 theoretically best fault localization techniques. For the remaining two, Ochiai also outperforms them, albeit not statistically significantly. This happens because an assumption in Xie et al.'s work is not satisfied in many fault localization settings.
format text
author LE, Tien-Duy B.
THUNG, Ferdian
LO, David
author_facet LE, Tien-Duy B.
THUNG, Ferdian
LO, David
author_sort LE, Tien-Duy B.
title Theory and practice, do they match? A case with spectrum-based fault localization
title_short Theory and practice, do they match? A case with spectrum-based fault localization
title_full Theory and practice, do they match? A case with spectrum-based fault localization
title_fullStr Theory and practice, do they match? A case with spectrum-based fault localization
title_full_unstemmed Theory and practice, do they match? A case with spectrum-based fault localization
title_sort theory and practice, do they match? a case with spectrum-based fault localization
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/2020
https://ink.library.smu.edu.sg/context/sis_research/article/3019/viewcontent/4981a380.pdf
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