eprintid: 10082880
rev_number: 26
eprint_status: archive
userid: 608
dir: disk0/10/08/28/80
datestamp: 2019-10-08 14:48:37
lastmod: 2021-10-11 22:45:45
status_changed: 2019-10-08 14:48:37
type: proceedings_section
metadata_visibility: show
creators_name: An, G
creators_name: Blot, A
creators_name: Petke, J
creators_name: Yoo, S
title: PyGGI 2.0: Language independent genetic improvement framework
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Software and its engineering, Software creation and management, Search-based software engineering
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2019-08-12
date_type: published
publisher: ACM
official_url: https://dx.doi.org/10.1145/3338906.3341184
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1697703
doi: 10.1145/3338906.3341184
isbn_13: 978-1-4503-5572-8
language_elements: English
lyricists_name: Blot, Aymeric
lyricists_name: Petke, Justyna
lyricists_id: ABLOT72
lyricists_id: JPETK66
actors_name: Petke, Justyna
actors_id: JPETK66
actors_role: owner
full_text_status: public
publication: ESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
place_of_pub: New York (NY), USA
pagerange: 1100-1104
event_title: The 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering 2019
event_location: Tallinn, Estonia
event_dates: 26th-30th August 2019
book_title: Proceedings of the 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering  2019
citation:        An, G;    Blot, A;    Petke, J;    Yoo, S;      (2019)    PyGGI 2.0: Language independent genetic improvement framework.                     In:  Proceedings of the 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering 2019.  (pp. pp. 1100-1104).  ACM: New York (NY), USA.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10082880/7/Petke_PyGGI%202.0.%20Language%20independent%20genetic%20improvement%20framework_AAM.pdf