# BoostPolyGlot: A Structured IR Generation-Based Fuzz Testing Framework for GCC Compiler Frontend
## 1 Introduction  
In the domain of computer science, compilers stand as pivotal enablers of code optimization and cross-platform programming. Compiler construction is a multifaceted process encompassing stages such as lexical analysis, syntax analysis, and semantic analysis. Lexical analysis converts the source program's character stream into lexical units, whereas syntax analysis constructs an Abstract Syntax Tree (AST) from these units. Semantic analysis ensures that syntactically valid code adheres to language specifications at the semantic level, verifying proper variable usage and type consistency. Following these analyses, the compiler performs code optimization to enhance the performance of the generated target code, including reducing execution time and memory footprint. The optimizationprocess employs numerous techniques, including constant folding, loop optimization, and function inlining. Notable compilers such as GCC not only translate source code into machine-executable files but also offer a suite of optimization options [1]. With ongoing advancements in compilation technology, compilers have achieved substantial breakthroughs in enhancing computing performance, security, and reliability. Nevertheless, due to the intricate nature of compiler architectures and the diversity of functional modules, improper handling of operations such as lexical analysis, syntax analysis, and semantic analysis may introduce compilation errors into the final executable program. These errors can precipitate critical security vulnerabilities during software execution, including memory leaks, buffer overflows, and logical flaws [2]. Fuzz testing [3; 4] is a technique for uncovering software vulnerabilities by providing unintended inputs to a target system and monitoring for anomalous outcomes. Compilers take a large volume of directly generated or mutated source code as input, and runtime crashes or memory-detection errors often indicate compilation defects, including those inherent in the compiler itself. In recent years, the field of fuzz testing has witnessed significant progress. Generation-based fuzz testing methods have been developed for programs with highly structured input formats. These methods focus on generating test cases that are valid in terms of syntax and semantics, often using techniques such as input models, grammar-based generation, or machine learning-driven approaches. Mutation-based fuzz testing, on the other hand, typically starts with an initial corpus of test cases and generates new ones through random mutations. Modern mutation-based fuzzers have integrated more advanced techniques like symbolic execution and taint analysis to improve their effectiveness. However, current compiler-focused fuzz testing is confronted with three critical challenges. First, generating syntactically correct test cases remains problematic: every programming language adheres to fundamental grammatical rules, and syntax errors in source code trigger \"syntax error\" prompts from the compiler's frontend parser, halting execution. Second, ensuring semantic correctness is equally challenging: semantic analysis verifies context-dependent properties of syntactically valid code, such as variable-usage relationships and compliance with language specifications. Existing fuzzing frameworks, however, lack contextual awareness during mutation, which may potentially lead to program termination. These two challenges lead to the rejection of test cases during the frontend processing phase, thereby precluding coverage of the deeper stages of compilation optimization. Furthermore, even when test cases traverse lexical, syntactic, and semantic analysis to form AST, convert to Intermediate Representation (IR), and enter the optimization pipeline, current tools inadequately consider optimization phase characteristics during generation and mutation. This deficiency causes test cases to fail to effectively cover critical code.