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FOT: a versatile, configurable, extensible fuzzing framework.

, , , , and . ESEC/SIGSOFT FSE, page 867-870. ACM, (2018)

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A First Look at the Effect of Deep Learning in Coverage-guided Fuzzing., , , , , , , and . ASE, page 1186-1189. IEEE, (2021)FOT: a versatile, configurable, extensible fuzzing framework., , , , and . ESEC/SIGSOFT FSE, page 867-870. ACM, (2018)Hawkeye: Towards a Desired Directed Grey-box Fuzzer., , , , , , and . ACM Conference on Computer and Communications Security, page 2095-2108. ACM, (2018)DiffChaser: Detecting Disagreements for Deep Neural Networks., , , , , and . IJCAI, page 5772-5778. ijcai.org, (2019)Steelix: program-state based binary fuzzing., , , , , and . ESEC/SIGSOFT FSE, page 627-637. ACM, (2017)An Empirical Study on Benchmarks of Artificial Software Vulnerabilities., , , , , and . CoRR, (2020)Leopard: identifying vulnerable code for vulnerability assessment through program metrics., , , , , , and . ICSE, page 60-71. IEEE / ACM, (2019)Locating vulnerabilities in binaries via memory layout recovering., , , , , , , and . ESEC/SIGSOFT FSE, page 718-728. ACM, (2019)Principled Greybox Fuzzing.. ICFEM, volume 11232 of Lecture Notes in Computer Science, page 455-458. Springer, (2018)BIFF: Practical Binary Fuzzing Framework for Programs of IoT and Mobile Devices., , , , , , and . ASE, page 1161-1165. IEEE, (2021)