The GISMOE challenge: Constructing the Pareto Program Surface Using Genetic Programming to Find Better Programs

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Harman:2012:ASE,
  author =       "Mark Harman and William B. Langdon and Yue Jia and 
                 David R. White and Andrea Arcuri and John A. Clark",
  title =        "The GISMOE challenge: Constructing the {Pareto}
                 Program Surface Using Genetic Programming to Find
                 Better Programs",
  booktitle =    "The 27th IEEE/ACM International Conference on
                 Automated Software Engineering (ASE 12)",
  year =         "2012",
  pages =        "1--14",
  address =      "Essen, Germany",
  publisher_address = "New York, NY, USA",
  month =        sep # " 3-7",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, Software
                 Engineering, Algorithms, Design, Experimentation, Human
                 Factors, Languages, Measurement, Performance,
                 Verification, SBSE, Search Based Optimisation,
                 Compilation, Non-functional Properties, Pareto
                 Surface",
  isbn13 =       "978-1-4503-1204-2",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/gismo/Harman_2012_ASE.pdf",
  DOI =          "doi:10.1145/2351676.2351678",
  acmid =        "2351678",
  size =         "14 pages",
  abstract =     "Optimising programs for non-functional properties such
                 as speed, size, throughput, power consumption and
                 bandwidth can be demanding; pity the poor programmer
                 who is asked to cater for them all at once! We set out
                 an alternate vision for a new kind of software
                 development environment inspired by recent results from
                 Search Based Software Engineering (SBSE). Given an
                 input program that satisfies the functional
                 requirements, the proposed programming environment will
                 automatically generate a set of candidate program
                 implementations, all of which share functionality, but
                 each of which differ in their non-functional trade
                 offs. The software designer navigates this diverse
                 Pareto surface of candidate implementations, gaining
                 insight into the trade offs and selecting solutions for
                 different platforms and environments, thereby
                 stretching beyond the reach of current compiler
                 technologies. Rather than having to focus on the
                 details required to manage complex, inter-related and
                 conflicting, non-functional tradeoffs, the designer is
                 thus freed to explore, to understand, to control and to
                 decide rather than to construct.",
  notes =        "This position paper accompanies the keynote given by
                 Mark Harman at the 27th IEEE/ACM International
                 Conference on Automated Software Engineering (ASE 12)
                 in Essen, Germany. It is joint work with Bill Langdon,
                 Yue Jia, David White, Andrea Arcuri and John Clark,
                 funded by the EPSRC grants SEBASE (EP/D050863,
                 EP/D050618 and EP/D052785), GISMO (EP/I033688) and
                 DAASE (EP/J017515/) and by EU project FITTEST
                 (257574).",
}

Genetic Programming entries for Mark Harman William B Langdon Yue Jia David Robert White Andrea Arcuri John A Clark

Citations