Program transformation landscapes for automated program modification using Gin
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Petke:2023:ESE,
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author = "Justyna Petke and Brad Alexander and Earl T. Barr and
Alexander E. I. Brownlee and Markus Wagner and
David R. White",
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title = "Program transformation landscapes for automated
program modification using {Gin}",
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journal = "Empirical Software Engineering",
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year = "2023",
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volume = "28",
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pages = "article no: 104",
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note = "Accepted to the journal-first track at ICSE 2024",
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keywords = "genetic algorithms, genetic programming, Genetic
improvement, Automated program modification, Automated
program repair, APR, Search-based software engineering,
SBSE",
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ISSN = "1382-3256",
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URL = "https://discovery.ucl.ac.uk/id/eprint/10170968/",
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URL = "https://rdcu.be/dJku8",
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DOI = "doi:10.1007/s10664-023-10344-5",
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code_url = "https://github.com/automatedprogrammodification/automatedprogrammodification/",
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size = "41 pages",
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abstract = "Automated program modification underlies two
successful research areas, genetic improvement and
program repair. Under the generate-and-validate
strategy, automated program modification transforms a
program, then validates the result against a test
suite. Much work has focused on the search space of
application of single fine-grained operators, copy,
delete, replace, and swap at both line and statement
granularity. This work explores the limits of this
strategy. We scale up existing findings an order of
magnitude from small corpora to 10 real-world Java
programs comprising up to 500k LoC. We decisively show
that the grammar-specificity of statement granular
edits pays off: its pass rate triples that of line
edits and uses 10% less computational resources. We
confirm previous findings that delete is the most
effective operator for creating test-suite equivalent
program variants. We go farther than prior work by
exploring the limits of delete 's effectiveness by
exhaustively applying it. We show this strategy is too
costly in practice to be used to search for improved
software variants. We further find that pass rates drop
from 12–34% for single statement edits to 2–6% for
5-edit sequences, which implies that further progress
will need human-inspired operators that target specific
faults or improvements. A program is amenable to
automated modification to the extent to which
automatically editing it is likely to produce
test-suite passing variants. We are the first to
systematically search for a code measure that
correlates with a program's amenability to automated
modification. We found no strong correlations, leaving
the question open.",
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notes = "University College London, London, UK",
- }
Genetic Programming entries for
Justyna Petke
Brad Alexander
Earl Barr
Alexander E I Brownlee
Markus Wagner
David Robert White
Citations