Evaluation of Genetic Improvement Tools for Improvement of Non-functional Properties of Software

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

@InProceedings{Zuo:2022:GI,

• author = "Shengjie Zuo and Aymeric Blot and Justyna Petke",
• title = "Evaluation of Genetic Improvement Tools for Improvement of Non-functional Properties of Software",
• booktitle = "GI @ GECCO 2022",
• year = "2022",
• editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke",
• pages = "1956--1965",
• address = "Boston, USA",
• publisher_address = "New York, NY, USA",
• month = "9 " # jul,
• organisation = "SIGEVO",
• publisher = "ACM",
• note = "Winner Best Paper",
• keywords = "genetic algorithms, genetic programming, genetic improvement, survey, tooling, non-functional properties, NFP",
• isbn13 = "978-1-4503-9268-6/22/07",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Zuo_2022_GI.pdf",
• DOI = "doi:10.1145/3520304.3534004",
• code_url = "http://geneticimprovementofsoftware.com/learn/survey",
• size = "10 pages",
• abstract = "Genetic improvement (GI) improves both functional properties of software, such as bug repair, and non-functional properties, such as execution time, energy consumption, or source code size. There are studies summarising and comparing GI tools for improving functional properties of software; however there is no such study for improvement of its non-functional properties using GI. Therefore, this research aims to survey and report on the existing GI tools for improvement of non-functional properties of software. We conducted a literature review of available GI tools, and ran multiple experiments on the found open-source tools to examine their usability. We applied a cross-testing strategy to check whether the available tools can work on different programs.

  Overall, we found 63 GI papers that use a GI tool to improve non-functional properties of software, within which 31 are accompanied with open-source code. We were able to successfully run nine GI tools, and found that ultimately only two, Gin and PyGGI, can be readily applied to new general software.",

• notes = "http://geneticimprovementofsoftware.com/events/gecco2022

  (1) Testing Gin with SAT4J, which is the software improved byPyGGI 2.0 in previous work \cite{blot:2021:tevc}

  (2) Testing PyGGI 2.0 with Gson, which is the software improvedby Gin in previous work \cite{Petke:2019:SSBSE}

  (3) Testing LocoGP with Gson.

  (4) Testing the tool for shader simplification with MiniSAT,which is the software used in previous work on a GISMO-based tool \cite{Petke:2014:EuroGP}

  (5) Testing the GISMO-based tool with RNAfold, which is the software improved by GGGP in previous work \cite{Langdon:2017:GI}

  (6) Testing the tool for OpenCV with MiniSAT.

  (7) Testing GGGP with MiniSAT.

  (8) Testing PowerGAUGE with MiniSAT

  https://github.com/gintool/gin GIN Maven Gradle

  https://github.com/coinse/pyggi

  LocoGP

  https://github.com/fabianishere/shadevolution

  GISMO

  OpenCV

  GGGP

  PowerGAUGE

  'Gin and PyGGI are the only two GI tools are application-agnostic and can be easily applied to improve new software.'

  GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Shengjie Zuo Aymeric Blot Justyna Petke

Citations