Performance Localisation

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Brendan Cody-Kenny and Michael O'Neill and 
                 Stephen Barrett",
  title =        "Performance Localisation",
  booktitle =    "GI-2018, ICSE workshops proceedings",
  year =         "2018",
  editor =       "Justyna Petke and Kathryn Stolee and 
                 William B. Langdon and Westley Weimer",
  pages =        "27--34",
  address =      "Gothenburg, Sweden",
  month =        "2 " # jun,
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, genetic
  isbn13 =       "978-1-4503-5753-1",
  URL =          "",
  DOI =          "doi:10.1145/3194810.3194815",
  size =         "8 pages",
  abstract =     "Profiling techniques highlight where performance
                 issues manifest and provide a starting point for
                 tracing cause back through a program. While people
                 diagnose and understand the cause of performance to
                 guide formulation of a performance improvement, we seek
                 automated techniques for highlighting performance
                 improvement opportunities to guide search

                 We investigate mutation-based approaches for
                 highlighting where a performance improvement is likely
                 to exist. For all modification locations in a program,
                 we make all possible modifications and analyse how
                 often modifications reduce execution count. We compare
                 the resulting code location rankings against rankings
                 derived using a profiler and find that mutation
                 analysis provides the higher accuracy in highlighting
                 performance improvement locations in a set of benchmark
                 problems, though at a much higher execution cost. We
                 see both approaches as complimentary and consider how
                 they may be used to further guide Genetic Programming
                 in finding performance improvements",
  notes =        "Slides:


                 GI-2018 part of

Genetic Programming entries for Brendan Cody-Kenny Michael O'Neill Stephen Barrett