Removing the Kitchen Sink from Software

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

@InProceedings{Landsborough:2015:gi,
  author =       "Jason Landsborough and Stephen Harding and 
                 Sunny Fugate",
  title =        "Removing the Kitchen Sink from Software",
  booktitle =    "Genetic Improvement 2015 Workshop",
  year =         "2015",
  editor =       "William B. Langdon and Justyna Petke and 
                 David R. White",
  pages =        "833--838",
  address =      "Madrid",
  publisher_address = "New York, NY, USA",
  month =        "11-15 " # jul,
  organisation = "SIGEvo",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Improvement",
  isbn13 =       "978-1-4503-3488-4",
  URL =          "http://geneticimprovement2015.com/wp-content/uploads/2015/05/removing_the_kitchen_sink_from_software.pdf",
  URL =          "http://doi.acm.org/10.1145/2739482.2768424",
  DOI =          "doi:10.1145/2739482.2768424",
  size =         "8 pages",
  abstract =     "We would all benefit if software were slimmer,
                 thinner, and generally only did what we needed and
                 nothing more. To this end, our research team has been
                 exploring methods for removing unused and undesirable
                 features from compiled programs. Our primary goal is to
                 improve software security by removing rarely used
                 features in order to decrease a program's attack
                 surface. This paper describes two different approaches
                 for thinning binary programs. The first approach
                 removes specific program features using dynamic tracing
                 as a guide. This approach is relatively safe, but is
                 only capable of removing code which is reachable in a
                 trace when an undesirable feature is enabled. The
                 second approach uses a genetic algorithm (GA) to mutate
                 a program until a suitable variant is found. Our
                 GA-based approach can potentially remove any code that
                 is not strictly required for proper execution, but may
                 break program semantics in unpredictable ways. We show
                 results of these approaches on a toy program and
                 real-world software and explore some of the
                 implications for software security.",
  notes =        "http://geneticimprovement2015.com/",
}

Genetic Programming entries for Jason Landsborough Stephen Harding Sunny Fugate

Citations