Software mutational robustness View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-07-28

AUTHORS

Eric Schulte, Zachary P. Fry, Ethan Fast, Westley Weimer, Stephanie Forrest

ABSTRACT

Neutral landscapes and mutational robustness are believed to be important enablers of evolvability in biology. We apply these concepts to software, defining mutational robustness to be the fraction of random mutations to program code that leave a program’s behavior unchanged. Test cases are used to measure program behavior and mutation operators are taken from earlier work on genetic programming. Although software is often viewed as brittle, with small changes leading to catastrophic changes in behavior, our results show surprising robustness in the face of random software mutations. The paper describes empirical studies of the mutational robustness of 22 programs, including 14 production software projects, the Siemens benchmarks, and four specially constructed programs. We find that over 30 % of random mutations are neutral with respect to their test suite. The results hold across all classes of programs, for mutations at both the source code and assembly instruction levels, across various programming languages, and bear only a limited relation to test suite coverage. We conclude that mutational robustness is an inherent property of software, and that neutral variants (i.e., those that pass the test suite) often fulfill the program’s original purpose or specification. Based on these results, we conjecture that neutral mutations can be leveraged as a mechanism for generating software diversity. We demonstrate this idea by generating a population of neutral program variants and showing that the variants automatically repair latent bugs. Neutral landscapes also provide a partial explanation for recent results that use evolutionary computation to automatically repair software bugs. More... »

PAGES

281-312

References to SciGraph publications

  • 1997. Through the labyrinth evolution finds a way: A silicon ridge in EVOLVABLE SYSTEMS: FROM BIOLOGY TO HARDWARE
  • 2003-01. An Introduction to MCMC for Machine Learning in MACHINE LEARNING
  • 2010-01. Mutational robustness can facilitate adaptation in NATURE
  • 2007-08. The distribution of fitness effects of new mutations in NATURE REVIEWS GENETICS
  • 1968-02. Evolutionary Rate at the Molecular Level in NATURE
  • 2011-08-05. Compiler-Generated Software Diversity in MOVING TARGET DEFENSE
  • 2002-03-28. CIL: Intermediate Language and Tools for Analysis and Transformation of C Programs in COMPILER CONSTRUCTION
  • 1982-03. Two notions of correctness and their relation to testing in ACTA INFORMATICA
  • 2008-12. Neutralism and selectionism: a network-based reconciliation in NATURE REVIEWS GENETICS
  • 2004-11. Biological robustness in NATURE REVIEWS GENETICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10710-013-9195-8

    DOI

    http://dx.doi.org/10.1007/s10710-013-9195-8

    DIMENSIONS

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