Repairing Programs with Semantic Code Search
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Ke:2015:ASE,
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author = "Yalin Ke and Kathryn T. Stolee and
Claire {Le Goues} and Yuriy Brun",
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title = "Repairing Programs with Semantic Code Search",
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booktitle = "30th IEEE/ACM International Conference on Automated
Software Engineering (ASE 2015)",
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year = "2015",
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editor = "Lars Grunske and Michael Whalen",
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pages = "295--306",
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address = "Lincoln, Nebraska, USA",
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month = nov # " 9-13",
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publisher = "IEEE Computer Society",
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keywords = "genetic algorithms, genetic programming, genetic
improvement, APR, SBSE",
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isbn13 = "978-1-5090-0025-8",
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URL = "http://people.cs.umass.edu/brun/pubs/pubs/Ke15ase.pdf",
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DOI = "doi:10.1109/ASE.2015.60",
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acmid = "2916260",
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size = "12 pages",
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abstract = "Automated program repair can potentially reduce
debugging costs and improve software quality but recent
studies have drawn attention to shortcomings in the
quality of automatically generated repairs. We propose
a new kind of repair that uses the large body of
existing open-source code to find potential fixes. The
key challenges lie in efficiently finding code
semantically similar (but not identical) to defective
code and then appropriately integrating that code into
a buggy program. We present SearchRepair, a repair
technique that addresses these challenges by (1)
encoding a large database of human-written code
fragments as SMT constraints on input-output behaviour,
(2) localising a given defect to likely buggy program
fragments and deriving the desired input-output
behavior for code to replace those fragments, (3) using
state-of-the-art constraint solvers to search the
database for fragments that satisfy that desired
behaviour and replacing the likely buggy code with
these potential patches, and (4) validating that the
patches repair the bug against program test suites. We
find that SearchRepair repairs 150 (19percent) of 778
benchmark C defects written by novice students, 20 of
which are not repaired by GenProg, TrpAutoRepair, and
AE. We compare the quality of the patches generated by
the four techniques by measuring how many independent,
not-used-during-repair tests they pass, and find that
SearchRepair-repaired programs pass 97.3percent of the
tests, on average, whereas GenProg-, TrpAutoRepair-,
and AE-repaired programs pass 68.7percent, 72.1percent,
and 64.2percent of the tests, respectively. We conclude
that SearchRepair produces higher-quality repairs than
GenProg, TrpAutoRepair, and AE, and repairs some
defects those tools cannot.",
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notes = "http://ase2015.unl.edu/#tab-papers",
- }
Genetic Programming entries for
Yalin Ke
Kathryn T Stolee
Claire Le Goues
Yuriy Brun
Citations