Evolving Suspiciousness Metrics From Hybrid Data Set for Boosting a Spectrum Based Fault Localization
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- @Article{Ajibode:2020:A,
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author = "Adekunle Akinjobi Ajibode and Ting Shu and
Zuohua Ding",
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title = "Evolving Suspiciousness Metrics From Hybrid Data Set
for Boosting a Spectrum Based Fault Localization",
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journal = "IEEE Access",
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year = "2020",
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volume = "8",
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pages = "198451--198467",
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keywords = "genetic algorithms, genetic programming, Measurement,
Debugging, Boosting, Debugging, fault localization,
SBFL",
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DOI = "doi:10.1109/ACCESS.2020.3035413",
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ISSN = "2169-3536",
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abstract = "Spectrum Based Fault Localization (SBFL) uses
different metrics called risk evaluation formula to
guide and pinpoint faults in debugging process. The
accuracy of a specific SBFL method may be limited by
the used formulae and program spectra. However, it has
been demonstrated recently that Genetic Programming
could be used to automatically design formulae directly
from the program spectra. Therefore, this article
presents Genetic Programming approach for proposing
risk evaluation formula with the inclusion of radicals
to evolve suspiciousness metric directly from the
program spectra. 92 faults from Unix utilities of SIR
repository and 357 real faults from Defect4J repository
were used. The approach combines these data sets, used
2percent of the total faults (113) to evolve the
formulae and the remaining 7percent (336) to validate
the effectiveness of the metrics generated by our
approach. The proposed approach then uses Genetic
Programming to run 30 evolution to produce different 30
metrics. The GP-generated metrics consistently
out-performed all the classic formulae in both single
and multiple faults, especially OP2 on average of
2.2percent in single faults and 3.4percent in multiple
faults. The experiment results conclude that the
combination of Hybrid data set and radical is a good
technique to evolve effective formulae for
spectra-based fault localization.",
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notes = "Also known as \cite{9247185}",
- }
Genetic Programming entries for
Adekunle Akinjobi Ajibode
Ting Shu
Zuohua Ding
Citations