Comparing Line and AST Granularity Level for Program Repair using PyGGI

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{An:2018:GI,

• author = "Gabin An and Jinhan Kim and Shin Yoo",
• title = "Comparing Line and {AST} Granularity Level for Program Repair using {PyGGI}",
• booktitle = "GI-2018, ICSE workshops proceedings",
• year = "2018",
• editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer",
• pages = "19--26",
• address = "Gothenburg, Sweden",
• month = "2 " # jun,
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE",
• isbn13 = "978-1-4503-5753-1",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2018/gi2018/papers/An_2018_GI.pdf",
• URL = "https://coinse.kaist.ac.kr/publications/pdfs/An2018to.pdf",
• DOI = "doi:10.1145/3194810.3194814",
• size = "8 pages",
• abstract = "PyGGI is a lightweight Python framework that can be used to implement generic Genetic Improvement algorithms at the API level. The original version of PyGGI only provided lexical modifications, i.e., modifications of the source code at the physical line granularity level. This paper introduces new extensions to PyGGI that enables syntactic modifications for Python code, i.e., modifications that operates at the AST granularity level. Taking advantage of the new extensions, we also present a case study that compares the lexical and syntactic search granularity level for automated program repair, using ten seeded faults in a real world open source Python project. The results show that search landscapes at the AST granularity level are more effective (i.e. eventually more likely to produce plausible patches) due to the smaller sizes of ingredient spaces (i.e., the space from which we search for the material to build a patch), but may require longer time for search because the larger number of syntactically intact candidates leads to more fitness evaluations.",
• notes = "Slides: http://geneticimprovementofsoftware.com/wp-content/uploads/2018/06/gi-pyggi.compressed.pdf

  GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018

  part of \cite{Petke:2018:ICSEworkshop}",

}

Genetic Programming entries for Gabin An Jinhan Kim Shin Yoo

Citations