Year: 2026
Type: conference-paper
Abstract: LLM-assisted decompilation improves readability and re-executability, but still underperforms on stripped C++ functions that use the Standard Template Library (STL).Compilation, optimization, and symbol stripping remove or obscure source-level semantics such as container types and library-call structure, while traditional decompiler output often fails to recover them.We present STILL, a structured semantic interface that predicts functionlevel STL container semantics from stripped control-flow graphs and renders them as compact hints for LLM refinement.On STL-BENCH, STILL predicts common containerlevel STL semantics, with the strongest crossdataset results for stable string and vector slices.On stripped HumanEval decompilation, these hints enable DeepSeek-chat refinement to reach 28.4% executability, compared with 17.4% for no-hint refinement and 8.9% for raw Ghidra decompilation; hint utility is downstream-backbone-dependent, with decompilation-specialized models requiring lightweight adaptation to benefit from the same interface.
Authors: Xiaohan Wang, Kevin Leach
Institution: Vanderbilt University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7204581320