方法对比
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| 模糊测试× | 符号执行× | |
|---|---|---|
| 领域 | 密码学 | 密码学 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1990 | 1976 |
| 提出者≠ | Barton Miller | James C. King |
| 类型≠ | random input-based testing technique | formal verification technique |
| 开创性文献≠ | Miller, B. P., Fredriksen, L., & So, B. (1990). An empirical study of the reliability of UNIX utilities. Communications of the ACM, 33(12), 32-44. DOI ↗ | King, J. C. (1976). Symbolic execution and program testing. Communications of the ACM, 19(7), 385-394. DOI ↗ |
| 别名 | fuzz testing, fuzzer, mutation testing | symbolic execution, symbolic analysis, concolic execution |
| 相关 | 3 | 3 |
| 摘要≠ | Fuzzing is a software testing technique that inputs large numbers of random or semi-random test cases to a program to find bugs, crashes, and security vulnerabilities. Pioneered by Barton Miller in 1990, fuzzing has become a primary method for discovering zero-day vulnerabilities in complex software. Modern fuzzing tools like libFuzzer, AFL, and HoneyPot combine coverage-guided mutation with instrumentation to efficiently explore program paths and trigger vulnerabilities. Fuzzing has discovered thousands of critical vulnerabilities in major software including browsers, compilers, and cryptographic libraries. | Symbolic execution is a program analysis technique that executes programs using symbolic (non-concrete) values instead of actual inputs, tracking how symbolic values flow through the program. Introduced by James C. King in 1976, symbolic execution builds mathematical constraints on program variables and can determine which inputs cause specific program behaviors, enabling automatic test generation and vulnerability detection. Modern symbolic execution tools like KLEE, S2E, and Z3 have become powerful instruments for finding subtle bugs and security vulnerabilities. |
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