Hashing fuzzing: introducing input diversity to improve crash detection

Menéndez, Héctor D. ORCID: https://orcid.org/0000-0002-6314-3725 and Clark, David (2022) Hashing fuzzing: introducing input diversity to improve crash detection. IEEE Transactions on Software Engineering, 48 (9) . pp. 3540-3553. ISSN 0098-5589 [Article] (doi:10.1109/TSE.2021.3100858)

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Abstract

The utility of a test set of program inputs is strongly influenced by its diversity and its size. Syntax coverage has become a standard proxy for diversity. Although more sophisticated measures exist, such as proximity of a sample to a uniform distribution, methods to use them tend to be type dependent. We use r-wise hash functions to create a novel, semantics preserving, testability transformation for C programs that we call HashFuzz. Use of HashFuzz improves the diversity of test sets produced by instrumentation-based fuzzers. We evaluate the effect of the HashFuzz transformation on eight programs from the Google Fuzzer Test Suite using four state-of-the-art fuzzers that have been widely used in previous research. We demonstrate pronounced improvements in the performance of the test sets for the transformed programs across all the fuzzers that we used. These include strong improvements in diversity in every case, maintenance or small improvement in branch coverage – up to 4.8% improvement in the best case, and significant improvement in unique crash detection numbers – between 28% to 97% increases compared to test sets for untransformed programs

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