A few works address the challenge of automating software diversification, and they all share one core idea: using automated test suites to drive diversification. However, there is is lack of solid understanding of how test suites, programs and transformations interact one with another in this process. We explore this intricate interplay in the context of a specific diversification technique called "sosiefication". Sosiefication generates sosie programs, i.e., variants of a program in which some statements are deleted, added or replaced but still pass the test suite of the original program. Our investigation of the influence of test suites on sosiefication exploits the following observation: test suites cover the different regions of programs in very unequal ways. Hence, we hypothesize that sosie synthesis has different performances on a statement that is covered by one hundred test case and on a statement that is covered by a single test case. We synthesize 24583 sosies on 6 popular open-source Java programs. Our results show that there are two dimensions for diversification. The first one lies in the specification: the more test cases cover a statement, the more difficult it is to synthesize sosies. Yet, to our surprise, we are also able to synthesize sosies on highly tested statements (up to 600 test cases), which indicates an intrinsic property of the programs we study. The second dimension is in the code: we manually explore dozens of sosies and characterize new types of forgiving code regions that are prone to diversification.
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.