research-article

PyGGI 2.0: language independent genetic improvement framework

Authors:

Shin YooAuthors Info & Claims

ESEC/FSE 2019: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 1100 - 1104

https://doi.org/10.1145/3338906.3341184

Published: 12 August 2019 Publication History

Abstract

PyGGI is a research tool for Genetic Improvement (GI), that is designed to be versatile and easy to use. We present version 2.0 of PyGGI, the main feature of which is an XML-based intermediate program representation. It allows users to easily define GI operators and algorithms that can be reused with multiple target languages. Using the new version of PyGGI, we present two case studies. First, we conduct an Automated Program Repair (APR) experiment with the QuixBugs benchmark, one that contains defective programs in both Python and Java. Second, we replicate an existing work on runtime improvement through program specialisation for the MiniSAT satisfiability solver. PyGGI 2.0 was able to generate a patch for a bug not previously fixed by any APR tool. It was also able to achieve 14% runtime improvement in the case of MiniSAT. The presented results show the applicability and the expressiveness of the new version of PyGGI. A video of the tool demo is at: https://youtu.be/PxRUdlRDS40.

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Cited By

Guizzo GWilliams DHarman MPetke JSarro F(2024)Speeding Up Genetic Improvement via Regression Test SelectionACM Transactions on Software Engineering and Methodology10.1145/368046633:8(1-31)Online publication date: 23-Jul-2024
https://dl.acm.org/doi/10.1145/3680466
Langdon WClark D(2024)Deep imperative mutations have less impactAutomated Software Engineering10.1007/s10515-024-00475-432:1Online publication date: 3-Dec-2024
https://doi.org/10.1007/s10515-024-00475-4
Callan JPetke J(2024)Multi-objective improvement of Android applicationsAutomated Software Engineering10.1007/s10515-024-00472-732:1Online publication date: 4-Nov-2024
https://doi.org/10.1007/s10515-024-00472-7
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Index Terms

PyGGI 2.0: language independent genetic improvement framework
1. Software and its engineering
  1. Software creation and management
    1. Search-based software engineering

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Published In

cover image ACM Conferences

ESEC/FSE 2019: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 2019

1264 pages

ISBN:9781450355728

DOI:10.1145/3338906

General Chairs:
Marlon Dumas
University of Tartu, Estonia
,
Dietmar Pfahl
University of Tartu, Estonia
,
Program Chairs:
Sven Apel
Saarland University, Germany
,
Alessandra Russo
Imperial College, UK

Copyright © 2019 ACM.

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Published: 12 August 2019

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ESEC/FSE '19

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ESEC/FSE '19: 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 26 - 30, 2019

Tallinn, Estonia

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Cited By

Guizzo GWilliams DHarman MPetke JSarro F(2024)Speeding Up Genetic Improvement via Regression Test SelectionACM Transactions on Software Engineering and Methodology10.1145/368046633:8(1-31)Online publication date: 23-Jul-2024
https://dl.acm.org/doi/10.1145/3680466
Langdon WClark D(2024)Deep imperative mutations have less impactAutomated Software Engineering10.1007/s10515-024-00475-432:1Online publication date: 3-Dec-2024
https://doi.org/10.1007/s10515-024-00475-4
Callan JPetke J(2024)Multi-objective improvement of Android applicationsAutomated Software Engineering10.1007/s10515-024-00472-732:1Online publication date: 4-Nov-2024
https://doi.org/10.1007/s10515-024-00472-7
Pinna GRavalico DRovito LManzoni LDe Lorenzo A(2024)Enhancing Large Language Models-Based Code Generation by Leveraging Genetic ImprovementGenetic Programming10.1007/978-3-031-56957-9_7(108-124)Online publication date: 28-Mar-2024
https://doi.org/10.1007/978-3-031-56957-9_7
Langdon WAlexander B(2023)Genetic Improvement of OLC and H3 with Magpie2023 IEEE/ACM International Workshop on Genetic Improvement (GI)10.1109/GI59320.2023.00011(9-16)Online publication date: May-2023
https://doi.org/10.1109/GI59320.2023.00011
Mesecan IBlackwell DClark DCohen MPetke J(2022)Keeping Secrets: Multi-objective Genetic Improvement for Detecting and Reducing Information LeakageProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3556947(1-12)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3556947
Zhong JHort MSarro FWagner M(2022)Py2CyProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534037(1950-1955)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534037
Krauss OWagner M(2022)AmaruProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534016(1930-1937)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534016
Zuo SBlot APetke JWagner M(2022)Evaluation of genetic improvement tools for improvement of non-functional properties of softwareProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534004(1956-1965)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534004
Blot APetke J(2021)Empirical Comparison of Search Heuristics for Genetic Improvement of SoftwareIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.307027125:5(1001-1011)Online publication date: Oct-2021
https://doi.org/10.1109/TEVC.2021.3070271
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