ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Yuan:ieeeSE,
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author = "Yuan Yuan and Wolfgang Banzhaf",
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journal = "IEEE Transactions on Software Engineering",
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title = "{ARJA}: Automated Repair of Java Programs via
Multi-Objective Genetic Programming",
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year = "2020",
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volume = "46",
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number = "10",
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pages = "1040--1067",
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month = oct,
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keywords = "genetic algorithms, genetic programming, genetic
improvement, Program repair, APR, patch generation,
multi-objective optimization",
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ISSN = "0098-5589",
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DOI = "doi:10.1109/TSE.2018.2874648",
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size = "28 pages",
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abstract = "Automated program repair is the problem of
automatically fixing bugs in programs in order to
significantly reduce the debugging costs and improve
the software quality. To address this problem,
test-suite based repair techniques regard a given test
suite as an oracle and modify the input buggy program
to make the entire test suite pass. GenProg is well
recognized as a prominent repair approach of this kind,
which uses genetic programming (GP) to rearrange the
statements already extant in the buggy program.
However, recent empirical studies show that the
performance of GenProg is not fully satisfactory,
particularly for Java. In this paper, we propose ARJA,
a new GP based repair approach for automated repair of
Java programs. To be specific, we present a novel
lower-granularity patch representation that properly
decouples the search subspaces of likely-buggy
locations, operation types and potential fix
ingredients, enabling GP to explore the search space
more effectively. Based on this new representation, we
formulate automated program repair as a multi-objective
search problem and use NSGA-II to look for simpler
repairs. To reduce the computational effort and search
space, we introduce a test filtering procedure that can
speed up the fitness evaluation of GP and three types
of rules that can be applied to avoid unnecessary
manipulations of the code. Moreover, we also propose a
type matching strategy that can create new potential
fix ingredients by exploiting the syntactic patterns of
existing statements. We conduct a large-scale empirical
evaluation of ARJA along with its variants on both
seeded bugs and real-world bugs in comparison with
several state-of-the-art repair approaches. Our results
verify the effectiveness and efficiency of the search
mechanisms employed in ARJA and also show its
superiority over the other approaches. In particular,
compared to jGenProg (an implementation of GenProg for
Java), an ARJA version fully following the redundancy
assumption can generate a test-suite adequate patch for
more than twice the number of bugs (from 27 to 59), and
a correct patch for nearly four times of the number
(from 5 to 18), on 224 real-world bugs considered in
Defects4J. Furthermore, ARJA is able to correctly fix
several real multi-location bugs that are hard to be
repaired by most of the existing repair approaches.",
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notes = "Also known as \cite{8485732}",
- }
Genetic Programming entries for
Yuan Yuan
Wolfgang Banzhaf
Citations