On the Utility of Marrying GIN and PMD for Improving Stack Overflow Code Snippets

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Misc{DBLP:journals/corr/abs-2202-01490,

• author = "Sherlock A. Licorish and Markus Wagner",
• title = "On the Utility of Marrying {GIN} and {PMD} for Improving {Stack Overflow} Code Snippets",
• howpublished = "ArXiv",
• year = "2022",
• month = "3 " # feb,
• keywords = "genetic algorithms, genetic programming, Genetic improvement, static analysis, Hybridisation",
• timestamp = "Thu, 17 Feb 2022 16:43:17 +0100",
• biburl = "https://dblp.org/rec/journals/corr/abs-2202-01490.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://arxiv.org/abs/2202.01490",
• size = "5 pages",
• 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.",
• notes = "Cited by \cite{Licorish:2022:GI}",

}

Genetic Programming entries for Sherlock A Licorish Markus Wagner

Citations