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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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",
• note = "keynote paper",
• keywords = "genetic algorithms, genetic programming, genetic improvement, APR, 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