Akarsh Ashok, Dinil Mon Divakaran, Vivek Balachandran · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.28212 · cited by 0 works (OpenAlex) · open-access PDF
ExeBench를 분석하고 테스트 믹서가 decompiler를 얼마나 충실하게 측정하는지 영향을 미치는 9 가지 문제를 식별합니다. 이들 중 3 개는 LLM decompilers가 ExeBench의 기능에서 C 코드를 복구하는 데 가장 널리 사용되는 벤치마크 중 하나입니다. 이들은 정수와 플로팅 포인트 유형이 혼합되면 정수와 플로팅 포인트 유형을 복구하는 불분명한 파라미터 순서이며, 최적화된 조립이 떨어질 수있는 사용되지 않은 파라미터, 그리고 함수의 조립 코드 외부에 저장되는 일정이 있습니다. 6 개의 추가적인 효과는 테스트 유효성에 영향을 미칩니다 : 반환되지 않는 C 코드, 위치 보유자 유형, 최적화에 의해 제
Machine translation (Meta M2M100); the English original follows.
ExeBench is one of the most widely used benchmarks for evaluating LLM-based decompilers. We analyze ExeBench and identify nine issues that affect how faithfully a test measures a decompiler. Three of them prevent LLM decompilers from recovering C code from ExeBench's function. These are ambiguous parameter order when integer and floating-point types are mixed, unused parameters that optimized assembly may drop, and constants stored outside the function's assembly code. Six more affect test validity: non-void functions without a return, placeholder types, static or inline functions removed by the optimizer, functions named main, trivial bodies and truncated definitions. We present ExeBench-FHD, a backward-compatible revision of ExeBench that adds 16 new fields: call-site assembly, data sections, test wrappers, linked-binary disassembly and five warning flags. In ExeBench, 38.9% of functions have at least one of the five flags; dropping records with any of the four warning flags (all except unused_param, which marks functions that the new wrappers make evaluable) keeps 63.9% of the corpus. In an evaluation at -O2 on four architectures, the new fields raise GPT 6 Sol's re-execution rate by up to 26.5%, 19.1% and 21.0% on functions that read data sections, mix parameter types and have unused parameters, respectively.
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Topics: Computer science, Fidelity, Code (set theory), Test (biology), Integer (computer science), Order (exchange)
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