Сравнение методов

	Фаззинг	Статическое тестирование безопасности приложений	Символьное исполнение
Область	Криптография	Криптография	Криптография
Семейство	Machine learning	Machine learning	Machine learning
Год появления	1990	2000s	1976
Автор метода	Barton Miller	Various researchers	James C. King
Тип	random input-based testing technique	source code vulnerability detection	formal verification technique
Основополагающий источник	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.	Chess, B., & West, J. (2007). Secure Programming with Static Analysis. Addison-Wesley Professional. ISBN: 978-0321424778	King, J. C. (1976). Symbolic execution and program testing. Communications of the ACM, 19(7), 385-394.
Другие названия	fuzz testing, fuzzer, mutation testing	SAST, white-box testing, source code analysis	symbolic execution, symbolic analysis, concolic execution
Связанные	3	3	3
Сводка	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.	Static Application Security Testing (SAST) is a security analysis technique that examines source code or compiled binaries without executing the program to identify vulnerabilities, code quality issues, and security flaws. Developed in the 2000s, SAST analyzes code structure, data flow, and control flow to detect potential bugs such as SQL injection, buffer overflows, and insecure cryptographic usage. SAST is widely integrated into development workflows as a shift-left security practice, enabling early detection of vulnerabilities before code reaches production.	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.