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Software mutational robustness


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.

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  1. This definition is not to be confused with the “equivalent mutants” of mutation testing, see Sect. 2.3.1.


  1. D. Ackley, Personal communication (2000)

  2. T. Ackling, B. Alexander, I. Grunert, Evolving patches for software repair. in Genetic and Evolutionary Computation (2011), pp. 1427–1434

  3. C. Andrieu, N. De Freitas, A. Doucet, M.I. Jordan, An introduction to MCMC for machine learning. Mach. Learn. 50(1–2), 5–43 (2003)

    Article  MATH  Google Scholar 

  4. J. Anvik, L. Hiew, G.C. Murphy, Coping with an open bug repository. in OOPSLA Workshop on Eclipse Technology eXchange (2005), pp. 35–39

  5. A. Arcuri, Evolutionary repair of faulty software. Appl. Soft Comput. 11(4), 3494–3514 (2011)

    Article  Google Scholar 

  6. W. Banzhaf, A. Leier, Evolution on neutral networks in genetic programming. in Genetic Programming Theory and Practice III (2006), pp. 207–221

  7. G. Barrantes, D. Ackley, S. Forrest, D. Stefanovic, Randomized instruction set randomization. ACM Trans. Inf. Syst. Secur. (TISSEC) 8(1), 3–40 (2005)

    Article  Google Scholar 

  8. E.G. Barrantes, D.H. Ackley, S. Forrest, D. Stefanovic, Randomized instruction set emulation. ACM Trans. Inf. Syst. Secur. 8(1), 3–40 (2005)

    Article  Google Scholar 

  9. E.G. Barrantes, D.H. Ackley, T.S. Palmer, D. Stefanovic, D.D. Zovi, Randomized instruction set emulation to disrupt binary code injection attacks. in Computer and Communications Security (2003), pp. 281–289

  10. S. Bhatkar, D. DuVarney, R. Sekar, Address obfuscation: an effcient approach to combat a broad range of memory error exploits. in USENIX Security Symposium (2003)

  11. C. Bienia, S. Kumar, J. Singh, K. Li, The parsec benchmark suite: characterization and architectural implications. in Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques (ACM, 2008), pp. 72–81

  12. T.A. Budd, D. Angluin, Two notions of correctness and their relation to testing. Acta Informatica 18, 31–45 (1982)

    Google Scholar 

  13. S. Ciliberti, O. Martin, A. Wagner, Innovation and robustness in complex regulatory gene networks. Proc. Natl. Acad. Sci. 104(34), 13–591 (2007)

    Article  Google Scholar 

  14. C. Cowan, H. Hinton, C. Pu, J. Walpole, The cracker patch choice: an analysis of post hoc security techniques. in Proceedings of the 23rd National Information Systems Security Conference (NISSC) (2000)

  15. B. Cox, D. Evans, A. Filipi, J. Rowanhill, W. Hu, J. Davidson, J. Knight, A. Nguyen-Tuong, J. Hiser, N-variant systems: a secretless framework for security through diversity. in USENIX Security Symposium (2006)

  16. V. Dallmeier, A. Zeller, B. Meyer, Generating fixes from object behavior anomalies. in Automated Software Engineering (2009), pp. 550–554

  17. R. DeMillo, A. Offutt, Constraint-based automatic test data generation. Trans. Softw. Eng. 17(9), 900–910 (1991)

    Article  Google Scholar 

  18. J. Draghi, T. Parsons, G. Wagner, J. Plotkin, Mutational robustness can facilitate adaptation. Nature 463(7279), 353–355 (2010)

    Article  Google Scholar 

  19. A. Eyre-Walker, P. Keightley, The distribution of fitness effects of new mutations. Nat. Rev. Genet. 8(8), 610–618 (2007)

    Article  Google Scholar 

  20. R. Feldt, Generating diverse software versions with genetic programming: an experimental study. IEE Proc. Softw. 145(6), 228–236 (1998)

    Article  Google Scholar 

  21. R.A. Fisher, The genetical theory of natural selection. (Oxford, Oxford, 1930)

  22. S. Forrest, S. Hofmeyr, A. Somayaji, The evolution of system-call monitoring. in Annual Computer Security Applications Conference (2008), pp. 418–430

  23. S. Forrest, A. Somayaji, D.H. Ackley, Building diverse computer systems. in Workshop on Hot Topics in Operating Systems (1997), pp. 67–72

  24. P.G. Frankl, S.N. Weiss, C. Hu, All-uses vs mutation testing: an experimental comparison of effectiveness. J. Syst. Softw. 38(3), 235–253 (1997)

    Article  Google Scholar 

  25. Z.P. Fry, W. Weimer, A human study of fault localization accuracy. in International Conference on Software Maintenance (2010), pp. 1–10

  26. D. Garlan, J. Kramer, A.L. Wolf, (eds.), Proceedings of the First Workshop on Self-Healing Systems, WOSS 2002, Charleston, South Carolina, USA, November 18–19, 2002 (ACM, 2002)

  27. T. Graves, M. Harrold, J. Kim, A. Porter, G. Rothermel, An empirical study of regression test selection techniques. Trans. Softw. Eng. Methodol. 10(2), 184–208 (2001)

    Article  MATH  Google Scholar 

  28. I. Harvey, A. Thompson, Through the labyrinth evolution finds a way: a silicon ridge. in Evolvable Systems: From Biology to Hardware, pp. 406–422, (1997)

  29. W. Hordijk, A measure of landscapes. Evolut. Comput. 4(4), 335–360 (1996)

    Article  Google Scholar 

  30. W.E. Howden, Reliability of the path analysis testing strategy. Softw. Eng. IEEE Trans. 2(3), 208–215 (1976)

    Google Scholar 

  31. M. Hutchins, H. Foster, T. Goradia, T. Ostrand, Experiments of the effectiveness of dataflow-and control flow-based test adequacy criteria. in International Conference on Software Engineering (1994), pp. 191–200

  32. M. Huynen, P. Stadler, W. Fontana, Smoothness within ruggedness: the role of neutrality in adaptation. Proc. Natl. Acad. Sci. 93(1), 397 (1996)

    Article  Google Scholar 

  33. IEEE security and privacy, special issue on IT monocultures. 7(1) (2009)

  34. T. Jackson, B. Salamat, A. Homescu, K. Manivannan, G. Wagner, A. Gal, S. Brunthaler, C. Wimmer, M. Franz, Compiler-generated software diversity. in Moving Target Defense (Springer, 2011), pp. 77–98

  35. Y. Jia, M. Harman, An analysis and survey of the development of mutation testing. Trans. Softw. Eng. 37(5), 649–678 (2011). doi:10.1109/TSE.2010.62

    Article  Google Scholar 

  36. G. Jin, L. Song, W. Zhang, S. Lu, B. Liblit, Automated atomicity-violation fixing. in Programming Language Design and Implementation (2011)

  37. E. Juergens, F. Deissenboeck, B. Hummel, S. Wagner, Do code clones matter? in International Conference on Software Engineering (2009), pp. 485–495

  38. R. Kafri, O. Dahan, J. Levy, Y. Pilpel, Preferential protection of protein interaction network hubs in yeast: evolved functionality of genetic redundancy. Proc. Natl Acad. Sci. 105(4), 1243–1248 (2008)

    Article  Google Scholar 

  39. T. Kamiya, S. Kusumoto, K. Inoue, Ccfinder: a multilinguistic token-based code clone detection system for large scale source code. IEEE Trans. Softw. Eng. 28, 654–670 (2002)

    Article  Google Scholar 

  40. G. Kc, A. Keromytis, V. Prevelakis, Countering code-injection attacks with instruction-set randomization. in Computer and Communications Security (2003), pp. 272–280

  41. M. Kim, L. Bergman, T. Lau, D. Notkin, An ethnographic study of copy and paste programming practices in oopl. in Empirical Software Engineering, 2004. ISESE’04. Proceedings 2004 International Symposium on (IEEE, 2004), pp. 83–92

  42. M. Kimura et al., Evolutionary rate at the molecular level. Nature 217(5129), 624 (1968)

    Article  Google Scholar 

  43. M. Kimura, The Neutral Theory of Molecular Evolution. (Cambridge University Press, Cambridge, 1985)

    Google Scholar 

  44. K. King, A. Offutt, A fortran language system for mutation-based software testing. Softw. Pract. Exp. 21(7), 685–718 (1991)

    Article  Google Scholar 

  45. H. Kitano, Biological robustness. Nat. Rev. Genet. 5(11), 826–837 (2004)

    Article  Google Scholar 

  46. J.C. Knight, P. Ammann, Issues influencing the use of n-version programming. in IFIP Congress (1989)

  47. J.C. Knight, P.E. Ammann, An experimental evaluation of simple methods for seeding program errors. in International Conference on Software Engineering (1985)

  48. J.C. Knight, N.G. Leveson, An experimental evaluation of the assumption of independence in multiversion programming. IEEE Trans. Softw. Eng. 12(1), 96–109 (1986)

    Google Scholar 

  49. J. Krinke, A study of consistent and inconsistent changes to code clones. in Working Conference on Reverse Engineering (IEEE Computer Society, 2007), pp. 170–178. doi:10.1109/WCRE.2007.7

  50. C. Le Goues, T. Nguyen, S. Forrest, W. Weimer, GenProg: a generic method for automated software repair. Trans. Softw. Eng. 38(1), 54–72 (2012)

    Article  Google Scholar 

  51. C. Le Goues, M. Dewey-Vogt, S. Forrest, W. Weimer, A systematic study of automated program repair: fixing 55 out of 105 bugs for 8 each. in International Conference on Software Engineering (2012), pp. 3–13

  52. R.E. Lenski, J. Barrick, C. Ofria, Balancing robustness and evolvability. PLoS Biol. 4(12), e428 (2006)

    Article  Google Scholar 

  53. B. Littlewood, D. Miller, Conceptual modelling of coincident failures in multiversion software. IEEE Trans. Softw. Eng. 15(12), 1596–1614 (1989)

    Article  MathSciNet  Google Scholar 

  54. C. Meiklejohn, D. Hartl, A single mode of canalization. Trends Ecol. Evol. 17(10), 468–473 (2002)

    Article  Google Scholar 

  55. L.A. Meyers, F.D. Ancel, M. Lachmann, Evolution of genetic potential. PLoS Comput. Biol. 1(3), e32 (2005)

    Article  Google Scholar 

  56. J. Miller, Cartesian genetic programming. in Cartesian Genetic Programming (2011), pp. 17–34

  57. S. Misailovic, D. Roy, M. Rinard, Probabilistically accurate program transformations. in Static Analysis (2011), pp. 316–333

  58. G.C. Necula, S. McPeak, S.P. Rahul, W. Weimer, Cil: An infrastructure for C program analysis and transformation. in International Conference on Compiler Construction (2002), pp. 213–228

  59. A. Offutt, W. Craft, Using compiler optimization techniques to detect equivalent mutants. Softw. Test. Verif. Reliab. 4(3), 131–154 (1994)

    Article  Google Scholar 

  60. A. Offutt, J. Pan, Automatically detecting equivalent mutants and infeasible paths. Softw. Test. Verif. Reliab. 7(3), 165–192 (1997)

    Article  Google Scholar 

  61. A. Offutt, A. Lee, G. Rothermel, R. Untch, C. Zapf, An experimental determination of sufficient mutant operators. Trans. Softw. Eng. Methodol. 5(2), 99–118 (1996)

    Article  Google Scholar 

  62. J. Offutt, Y. Ma, Y. Kwon, The class-level mutants of mujava. in International Workshop on Automation of Software Test (ACM, 2006), pp. 78–84

  63. C. Ofria, W. Huang, E. Torng, On the gradual evolution of complexity and the sudden emergence of complex features. Artif. Life 14(3), 255–263 (2008)

    Article  Google Scholar 

  64. M. Orlov, M. Sipper, Flight of the FINCH through the Java wilderness. Trans. Evolut. Comput. 15(2), 166–192 (2011)

    Article  Google Scholar 

  65. M. Orlov, M. Sipper, Genetic programming in the wild: Evolving unrestricted bytecode. in Genetic and Evolutionary Computation Conference, (2009), pp. 1043–1050

  66. J.H. Perkins, S. Kim, S. Larsen, S. Amarasinghe, J. Bachrach, M. Carbin, C. Pacheco, F. Sherwood, S. Sidiroglou, G. Sullivan, W.F. Wong, Y. Zibin, M.D. Ernst, M. Rinard, Automatically patching errors in deployed software. in Symposium on Operating Systems Principles (2009)

  67. A. Poon, B.H. Davis, L. Chao, The coupon collector and the suppressor mutation estimating the number of compensatory mutations by maximum likelihood. Genetics 170(3), 1323–1332 (2005)

    Article  Google Scholar 

  68. M.C. Rinard, C. Cadar, D. Dumitran, D.M. Roy, T. Leu, W.S. Beebee, Enhancing server availability and security through failure-oblivious computing. in Operating Systems Design and Implementation (2004), pp. 303–316

  69. D. Robson, H. Regier, Sample size in petersen mark–recapture experiments. Trans. Am. Fish. Soc. 93(3), 215–226 (1964)

    Article  Google Scholar 

  70. G. Rothermel, M. Harrold, Empirical studies of a safe regression test selection technique. Trans. Softw. Eng. 24(6), 401–419 (1998)

    Article  Google Scholar 

  71. D. Schuler, A. Zeller, (un-) covering equivalent mutants. in Software Testing, Verification and Validation (ICST), 2010 Third International Conference on (IEEE, 2010), pp. 45–54

  72. E. Schulte, J. DiLorenzo, S. Forrest, W. Weimer, Automated repair of binary and assembly programs for cooperating embedded devices. in Architectural Support for Programming Languages and Operating Systems (2013)

  73. E. Schulte, S. Forrest, W. Weimer, Automatic program repair through the evolution of assembly code. in Automated Software Engineering (2010), pp. 33–36

  74. P. Schuster, W. Fontana, P. Stadler, I. Hofacker, From sequences to shapes and back: a case study in rna secondary structures. in Proceedings: Biological Sciences (1994), pp. 279–284

  75. S. Segura, R. Hierons, D. Benavides, A. Ruiz-Cortés, Mutation testing on an object-oriented framework: an experience report. Inf. Softw. Technol. 53(10), 1124–1136 (2011)

  76. R. Sharma, E.S., A. Aiken, Stochastic superoptimization. in Proceedings of the Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ACM, 2013)

  77. S. Sidiroglou, O. Laadan, C. Perez, N. Viennot, J. Nieh, A.D. Keromytis, Assure: automatic software self-healing using rescue points. in Architectural Support for Programming Languages and Operating Systems (2009), pp. 37–48

  78. P. Sitthi-Amorn, N. Modly, W. Weimer, J. Lawrence, Genetic programming for shader simplification. ACM Trans. Graph. 30(5), 152 (2011)

    Google Scholar 

  79. V. Smith, K. Chou, D. Lashkari, D. Botstein, P. Brown et al., Functional analysis of the genes of yeast chromosome v by genetic footprinting. Science 274(5295), 2069–2074 (1996)

    Article  Google Scholar 

  80. T. Smith, P. Husbands, P. Layzell, M. O’Shea, Fitness landscapes and evolvability. Evolut. Comput. 10(1), 1–34 (2002)

    Article  Google Scholar 

  81. T. Smith, P. Husbands, M. O’Shea, Neutral networks and evolvability with complex genotype-phenotype mapping. in Advances in Artificial Life (2001), pp. 272–281

  82. G.E. Suh, J.W. Lee, D. Zhang, S. Devadas, Secure program execution via dynamic information flow tracking. in ACM SIGPLAN Notices, vol. 39, (ACM, 2004), pp. 85–96

  83. R. Untch, Schema-Based Mutation Analysis: A New Test Data Adequacy Assessment Method. Ph.D. thesis. (Clemson University, Clemson, 1995)

  84. E. Van Nimwegen, J. Crutchfield, M. Huynen, Neutral evolution of mutational robustness. Proc. Natl. Acad. Sci. 96(17), 9716 (1999)

    Article  Google Scholar 

  85. V. Vassilev, J. Miller, The advantages of landscape neutrality in digital circuit evolution. in Evolvable systems: from biology to hardware (2000), pp. 252–263

  86. F. Vokolos, P. Frankl, Empirical evaluation of the textual differencing regression testing technique. in International Conference on Software Maintenance (1998), pp. 44–53

  87. A. Wagner, Robustness and Evolvability in Living Systems (Princeton Studies in Complexity). (Princeton University Press, Princeton, 2005)

    Google Scholar 

  88. A. Wagner, Neutralism and selectionism: a network-based reconciliation. Nat. Rev. Genet. 9(12), 965–974 (2008)

    Article  Google Scholar 

  89. A. Wagner, Robustness and evolvability: a paradox resolved. Proc. R. Soc. B Biol. Sci. 275(1630), 91 (2008)

    Article  Google Scholar 

  90. K.S.H.T. Wah, A theoretical study of fault coupling. Softw. Test. Verif. Reliability 10(1), 3–45 (2000)

    Article  Google Scholar 

  91. W. Weimer, Patches as better bug reports. in Generative Programming and Component Engineering (2006), pp. 181–190

  92. W. Weimer, T. Nguyen, C. Le Goues, S. Forrest, Automatically finding patches using genetic programming. in International Conference on Software Engineering (2009), pp. 364–367

  93. Y. Wei, Y. Pei, C.A. Furia, L.S. Silva, S. Buchholz, B. Meyer, A. Zeller, Automated fixing of programs with contracts. in International Symposium on Software Testing and Analysis (2010), pp. 61–72

  94. E. Weyuker, On testing non-testable programs. Comput. J. 25(4), 465–470 (1982)

    Article  Google Scholar 

  95. J.L. Wilkerson, D. Tauritz, Coevolutionary automated software correction. in Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (ACM, 2010), pp. 1391–1392

  96. D. Williams, W. Hu, J.W. Davidson, J. Hiser, J.C. Knight, A. Nguyen-Tuong, Security through diversity: leveraging virtual machine technology. IEEE Secur. Priv. 7(1), 26–33 (2009)

    Article  Google Scholar 

  97. X. Yin, J.C. Knight, W. Weimer, Exploiting refactoring in formal verification. in International Conference on Dependable Systems and Networks (2009), pp. 53–62

  98. E. Zuckerkandl, L. Pauling, Molecular disease, evolution and genetic heterogeneity. in Horizons in Biochemistry (1962)

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The authors would like to thank Lauren Ancel Meyers, William B. Langdon and Peter Schuster for their thoughtful comments. This work was supported by the National Science Foundation (SHF-0905236), Air Force Office of Scientific Research (FA9550-07-1-0532, FA9550-10-1-0277), DARPA (P-1070-113237), DOE (DE-AC02-05CH11231) and the Santa Fe Institute.

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Schulte, E., Fry, Z.P., Fast, E. et al. Software mutational robustness. Genet Program Evolvable Mach 15, 281–312 (2014).

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  • Mutational robustness
  • Genetic programming
  • Mutation testing
  • Proactive diversity
  • N-version programming
  • Neutral landscapes