Estimating the potential of program repair search spaces with commit analysis

Created by W.Langdon from gp-bibliography.bib Revision:1.8592

@Article{2007.06986,

• author = "Khashayar Etemadi and Niloofar Tarighat and Siddharth Yadav and Matias Martinez and Martin Monperrus",
• title = "Estimating the potential of program repair search spaces with commit analysis",
• journal = "Journal of Systems and Software",
• year = "2022",
• volume = "188",
• pages = "111263",
• month = jun,
• keywords = "genetic algorithms, genetic programming, genetic improvement, genprog, Program repair, APR, LighteR, GitHub, Search-space, Static code analysis, Commit analysis",
• ISSN = "0164-1212",
• URL = "http://arxiv.org/pdf/2007.06986",
• URL = "https://www.sciencedirect.com/science/article/pii/S0164121222000309",
• DOI = "doi:10.1016/j.jss.2022.111263",
• size = "17 pages",
• abstract = "The most natural method for evaluating program repair systems is to run them on bug datasets, such as Defects4J. Yet, using this evaluation technique on arbitrary real-world programs requires heavy configuration. we propose a purely static method to evaluate the potential of the search space of repair approaches. This new method enables researchers and practitioners to encode the search spaces of repair approaches and select potentially useful ones without struggling with tool configuration and execution. We encode the search spaces by specifying the repair strategies they employ. Next, we use the specifications to check whether past commits lie in repair search spaces. For a repair approach, including many human-written past commits in its search space indicates its potential to generate useful patches. We implement our evaluation method in LighteR. LighteR gets a Git repository and outputs a list of commits whose source code changes lie in repair search spaces. We run LighteR on 55309 commits from the history of 72 Github repositories with and show that LighteR precision and recall are 77 percent and 92 percent, respectively. Overall, our experiments show that our novel method is both lightweight and effective to study the search space of program repair approaches.",
• notes = "Also known as \cite{ETEMADI2022111263} See also \cite{arXiv-2007.06986}",

}

Genetic Programming entries for Khashayar Etemadi Niloofar Tarighat Siddharth Yadav Matias Sebastian Martinez Martin Monperrus

Citations