Understanding Automatically-Generated Patches Through Symbolic Invariant Differences
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{DBLP:conf/kbse/CashinMWF19,
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author = "Padraic Cashin and Carianne Martinez and
Westley Weimer and Stephanie Forrest",
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title = "Understanding Automatically-Generated Patches Through
Symbolic Invariant Differences",
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booktitle = "34th {IEEE/ACM} International Conference on Automated
Software Engineering, ASE 2019",
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year = "2019",
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pages = "411--414",
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address = "San Diego, CA, USA",
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month = nov # " 11-15",
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keywords = "genetic algorithms, genetic programming, GenProg,
daikon, APR",
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timestamp = "Sun, 19 Jan 2020 15:19:48 +0100",
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biburl = "https://dblp.org/rec/conf/kbse/CashinMWF19.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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URL = "https://doi.org/10.1109/ASE.2019.00046",
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DOI = "doi:10.1109/ASE.2019.00046",
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size = "4 pages",
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abstract = "Developer trust is a major barrier to the deployment
of automatically-generated patches. Understanding the
effect of a patch is a key element of that trust. We
find that differences in sets of formal invariants
characterize patch differences and that
implication-based distances in invariant space
characterize patch similarities. When one patch is
similar to another it often contains the same changes
as well as additional behaviour; this pattern is
well-captured by logical implication. We can measure
differences using a theorem prover to verify
implications between invariants implied by separate
programs. Although effective, theorem provers are
computationally intensive; we find that string distance
is an efficient heuristic for implication-based
distance measurements. We propose to use distances
between patches to construct a hierarchy highlighting
patch similarities. We evaluated this approach on over
300 patches and found that it correctly categorises
programs into semantically similar clusters. Clustering
programs reduces human effort by reducing the number of
semantically distinct patches that must be considered
by over 50percent, thus reducing the time required to
establish trust in automatically generated repairs.",
- }
Genetic Programming entries for
Padraic Cashin
Carianne Martinez
Westley Weimer
Stephanie Forrest
Citations