Satisfiability modulo fuzzing: a synergistic combination of SMT solving and fuzzing.

Proc. ACM Program. Lang.(2022)

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Programming languages and software engineering tools routinely encounter components that are difficult to reason on via formal techniques or whose formal semantics are not even available—third-party libraries, inline assembly code, SIMD instructions, system calls, calls to machine learning models, etc. However, often access to these components is available as input-output oracles—interfaces are available to query these components on certain inputs to receive the respective outputs. We refer to such functions as closed-box functions . Regular SMT solvers are unable to handle such closed-box functions. We propose Sādhak, a solver for SMT theories modulo closed-box functions. Our core idea is to use a synergistic combination of a fuzzer to reason on closed-box functions and an SMT engine to solve the constraints pertaining to the SMT theories. The fuzz and the SMT engines attempt to converge to a model by exchanging a rich set of interface constraints that are relevant and interpretable by them. Our implementation, Sādhak, demonstrates a significant advantage over the only other solver that is capable of handling such closed-box constraints: Sādhak solves 36.45% more benchmarks than the best-performing mode of this state-of-the-art solver and has 5.72x better PAR-2 score; on the benchmarks that are solved by both tools, Sādhak is (on an average) 14.62x faster.
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