QSYM : A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing

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The Art, Science, and Engineering of Fuzzing: A Survey (2018, background): “DeadlockFuzzer [116] FairFuzz [136] † † go-fuzz [215] Hawkeye [53] honggfuzz [204] kAFL [184] LibFuzzer [7] MagicFuzzer [47] Nautilus [22] RaceFuzzer [190] RedQueen [23] Steelix [138] † † † Syzkaller [216] Angora [56] + Cyberdyne [92] + DigFuzz [239] + Driller [200] + QSYM [230] + T-Fuzz [170] + † V”
Learning to Fuzz from Symbolic Execution with Application to Smart Contracts (2019, background): “Symbolic execution has been used to generate effective inputs as it can leverage off-the-shelf constraint solvers to solve path constraints which steer program execution along specific paths that improve coverage or expose new vulnerabilities [13, 14, 52, 67].”
Fuzzing: A Survey for Roadmap (2022, background): “To mitigate the challenge, the constraint solver only symbolizes the path constraints that are influenced by inputs [46, 204].”
sFuzz: An Efficient Adaptive Fuzzer for Solidity Smart Contracts (2020, background): “m, Jun Sun, Yun Lin, and Quang Tran Minh complements existing testing engines based on symbolic execution like Oyente and teEther, as it is known that fuzzing and symbolic execution are complementary [30, 32]. While AFL-based fuzzing is often effective, it has its limitation as well, i.e., it is oft”