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Publication Detail
PyGGI 2.0: Language independent genetic improvement framework
  • Publication Type:
  • Authors:
    An G, Blot A, Petke J, Yoo S
  • Publisher:
  • Publication date:
  • Pagination:
    1100, 1104
  • Published proceedings:
    ESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
  • ISBN-13:
  • Status:
  • Name of conference:
    The 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
  • Conference place:
    Tallinn, Estonia
  • Conference start date:
  • Conference finish date:
© 2019 ACM. 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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