The case for software evolution

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@InProceedings{DBLP:conf/sigsoft/GouesFW10,
  author =       "Claire {Le Goues} and Stephanie Forrest and 
                 Westley Weimer",
  title =        "The case for software evolution",
  booktitle =    "Proceedings of the FSE/SDP workshop on Future of
                 software engineering research, FoSER'10",
  year =         "2010",
  editor =       "Gruia-Catalin Roman and Kevin J. Sullivan",
  pages =        "205--210",
  address =      "Santa Fe, New Mexico, USA",
  publisher_address = "New York, NY, USA",
  month =        nov # " 7-11",
  organisation = "ACM SIGSOFT",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, sbse,
                 evolutionary computation, program repair, software
                 engineering",
  isbn13 =       "978-1-4503-0427-6",
  URL =          "http://www.cs.virginia.edu/~weimer/p/p205-legoues.pdf",
  DOI =          "doi:10.1145/1882362.1882406",
  size =         "5 pages",
  acmid =        "1882406",
  abstract =     "Many software systems exceed our human ability to
                 comprehend and manage, and they continue to contain
                 unacceptable errors. This is an unintended consequence
                 of Moore's Law, which has led to increases in system
                 size, complexity, and interconnectedness. Yet, software
                 is still primarily created, modified, and maintained by
                 humans. The interactions among heterogeneous programs,
                 machines and human operators has reached a level of
                 complexity rivalling that of some biological
                 ecosystems. By viewing software as an evolving complex
                 system, researchers could incorporate biologically
                 inspired mechanisms and employ the quantitative
                 analysis methods of evolutionary biology. This approach
                 could improve our understanding and analysis of
                 software; it could lead to robust methods for
                 automatically writing, debugging and improving code;
                 and it could improve predictions about functional and
                 structural transitions as scale increases. In the short
                 term, an evolutionary perspective challenges several
                 research assumptions, enabling advances in error
                 detection, correction, and prevention.",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
}

Genetic Programming entries for Claire Le Goues Stephanie Forrest Westley Weimer

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