SOSRepair: Expressive Semantic Search for Real-World Program Repair
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Afzal:2021:TSE,
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author = "Afsoon Afzal and Manish Motwani and
Kathryn T. Stolee and Yuriy Brun and Claire {Le Goues}",
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title = "{SOSRepair}: Expressive Semantic Search for Real-World
Program Repair",
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journal = "IEEE Transactions on Software Engineering",
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year = "2021",
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volume = "47",
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number = "10",
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pages = "2162--2181",
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keywords = "genetic algorithms, genetic programming, genetic
improvement, APR",
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ISSN = "0098-5589",
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URL = "https://doi.org/10.1109/TSE.2019.2944914",
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DOI = "doi:10.1109/TSE.2019.2944914",
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abstract = "Automated program repair holds the potential to
significantly reduce software maintenance effort and
cost. However, recent studies have shown that it often
produces low-quality patches that repair some but break
other functionality. We hypothesize that producing
patches by replacing likely faulty regions of code with
semantically-similar code fragments, and doing so at a
higher level of granularity than prior approaches can
better capture abstraction and the intended
specification, and can improve repair quality. We
create SOSRepair, an automated program repair technique
that uses semantic code search to replace candidate
buggy code regions with behaviorally-similar (but not
identical) code written by humans. SOSRepair is the
first such technique to scale to real-world defects in
real-world systems. On a subset of the ManyBugs
benchmark of such defects, SOSRepair produces patches
for 23 (35percent) of the 65 defects, including 3, 5,
and 8 defects for which previous state-of-the-art
techniques Angelix, Prophet, and GenProg do not,
respectively. On these 23 defects, SOSRepair produces
more patches (8, 35percent) that pass all independent
tests than the prior techniques. We demonstrate a
relationship between patch granularity and the ability
to produce patches that pass all independent tests. We
then show that fault localization precision is a key
factor in SOSRepair's success. Manually improving fault
localisation allows SOSRepair to patch 24 (37percent)
defects, of which 16 (67percent) pass all independent
tests. We conclude that (1) higher-granularity,
semantic-based patches can improve patch quality, (2)
semantic search is promising for producing high-quality
real-world defect repairs, (3) research in fault
localization can significantly improve the quality of
program repair techniques, and (4) semi-automated
approaches in which developers suggest fix locations
may produce high-quality patches.",
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notes = "Also known as \cite{8854217}",
- }
Genetic Programming entries for
Afsoon Afzal
Manish Motwani
Kathryn T Stolee
Yuriy Brun
Claire Le Goues
Citations