Investigating the Evolvability of Page Load Time

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

@InProceedings{Cody-Kenny:2018:evoApplications,
  author =       "Brendan Cody-Kenny and Umberto Manganiello and 
                 John Farrelly and Adrian Ronayne and Eoghan Considine and 
                 Thomas McGuire and Michael O'Neill",
  title =        "Investigating the Evolvability of Page Load Time",
  booktitle =    "21st International Conference on the Applications of
                 Evolutionary Computation, EvoSET 2018",
  year =         "2018",
  editor =       "Anna I. Esparcia-Alcazar and Sara Silva",
  series =       "LNCS",
  volume =       "10784",
  publisher =    "Springer",
  pages =        "769--777",
  address =      "Parma, Italy",
  month =        "4-6 " # apr,
  organisation = "Species",
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, Search-based software engineering, SBSE,
                 Javascript, Performance, Web applications",
  isbn13 =       "978-3-319-77537-1",
  DOI =          "doi:10.1007/978-3-319-77538-8_51",
  size =         "9 pages",
  abstract =     "Client-side Javascript execution environments
                 (browsers) allow anonymous functions and event-based
                 programming concepts such as callbacks. We investigate
                 whether a mutate-and-test approach can be used to
                 optimise web page load time in these environments.
                 First, we characterise a web page load issue in a
                 benchmark web page and derive performance metrics from
                 page load event traces.We parse Javascript source code
                 to an AST and make changes to method calls which appear
                 in a web page load event trace.We present an operator
                 based solely on code deletion and evaluate an existing
                 community-contributed performance optimising code
                 transform. By exploring Javascript code changes and
                 exploiting combinations of non-destructive changes, we
                 can optimise page load time by 41percent in our
                 benchmark web page.",
  notes =        "EvoApplications2018 held in conjunction with
                 EuroGP'2018 EvoCOP2018 and EvoMusArt2018
                 http://www.evostar.org/2018/cfp_evoapps.php",
}

Genetic Programming entries for Brendan Cody-Kenny Umberto Manganiello John Farrelly Adrian Ronayne Eoghan Considine Thomas McGuire Michael O'Neill

Citations