On the Utility of Marrying GIN and PMD for Improving Stack Overflow Code Snippets
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Misc{DBLP:journals/corr/abs-2202-01490,
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author = "Sherlock A. Licorish and Markus Wagner",
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title = "On the Utility of Marrying {GIN} and {PMD} for
Improving {Stack Overflow} Code Snippets",
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howpublished = "ArXiv",
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year = "2022",
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month = "3 " # feb,
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keywords = "genetic algorithms, genetic programming, Genetic
improvement, static analysis, Hybridisation",
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timestamp = "Thu, 17 Feb 2022 16:43:17 +0100",
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biburl = "https://dblp.org/rec/journals/corr/abs-2202-01490.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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URL = "https://arxiv.org/abs/2202.01490",
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size = "5 pages",
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abstract = "Software developers are increasingly dependent on
question and answer portals and blogs for coding
solutions. While such interfaces provide useful
information, there are concerns that code hosted here
is often incorrect, insecure or incomplete. Previous
work indeed detected a range of faults in code provided
on Stack Overflow through the use of static analysis.
Static analysis may go a far way towards quickly
establishing the health of software code available
online. In addition, mechanisms that enable rapid
automated program improvement may then enhance such
code. Accordingly, we present this proof of concept. We
use the PMD static analysis tool to detect performance
faults for a sample of Stack Overflow Java code
snippets, before performing mutations on these snippets
using GIN. We then re-analyse the performance faults in
these snippets after the GIN mutations. GIN
RandomSampler was used to perform 17986 unique line and
statement patches on 3034 snippets where PMD violations
were removed from 770 patched versions. Our outcomes
indicate that static analysis techniques may be
combined with automated program improvement methods to
enhance publicly available code with very little
resource requirements. We discuss our planned research
agenda in this regard.",
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notes = "Cited by \cite{Licorish:2022:GI}",
- }
Genetic Programming entries for
Sherlock A Licorish
Markus Wagner
Citations