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| Symbolische Ausführung× | Fuzzing× | |
|---|---|---|
| Fachgebiet | Kryptographie | Kryptographie |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1976 | 1990 |
| Urheber≠ | James C. King | Barton Miller |
| Typ≠ | formal verification technique | random input-based testing technique |
| Wegweisende Quelle≠ | King, J. C. (1976). Symbolic execution and program testing. Communications of the ACM, 19(7), 385-394. DOI ↗ | 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 ↗ |
| Aliasnamen | symbolic execution, symbolic analysis, concolic execution | fuzz testing, fuzzer, mutation testing |
| Verwandt | 3 | 3 |
| Zusammenfassung≠ | 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. | 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. |
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