关键词: 编译器测试, 模糊测试, 大语言模型, 测试用例生成, 差分测试

Abstract:

To address the common issues in the existing compiler fuzzing， such as insufficient test case diversity， high testing costs， and heavy reliance on manual intervention， a multi-strategy compiler fuzzing system based on Large Language Models （LLMs）， DHFUZZ （Deep & Hybrid FUZZer）， was proposed. In the system， multi-strategy test case generation was integrated with LLMs， thereby adjusting prompts to guide the LLM in generating diverse test cases， and the custom code generators were combined to construct test code with specific functions rapidly. Additionally， mutation strategies were incorporated to further enhance test case diversity and improve code coverage， thereby exploring potential compiler execution paths more effectively， so as to trigger deep vulnerabilities. At the same time， an end-to-end automated testing process was designed to fully automate the process from test case generation and compiler execution to bug detection. Experimental results demonstrate that DHFUZZ can generate test cases covering diverse syntactic structures and semantic features， and improves code coverage significantly in tests on mainstream compilers such as GCC （GNU Compiler Collection） and Clang， demonstrating its vulnerability detection potential， and providing an efficient and low-cost automated testing solution for compiler reliability assurance.

Key words: compiler testing, fuzzing, Large Language Model (LLM), test case generation, differential testing