Decompilation transforms compiled code back into a high-level programming
language for analysis when source code is unavailable. Previous work has
primarily focused on enhancing decompilation performance by increasing the
scale of model parameters or training data for pre-training. Based on the
characteristics of the decompilation task, we propose two methods: (1) Without
fine-tuning, the Self-Constructed Context Decompilation (sc$^2$dec) method
recompiles the LLM's decompilation results to construct pairs for in-context
learning, helping the model improve decompilation performance. (2) Fine-grained
Alignment Enhancement (FAE), which meticulously aligns assembly code with
source code at the statement level by leveraging debugging information, is
employed during the fine-tuning phase to achieve further improvements in
decompilation. By integrating these two methods, we achieved a Re-Executability
performance improvement of approximately 7.35\% on the Decompile-Eval
benchmark, establishing a new state-of-the-art performance of 55.03\%.

该研究论文提出了两种方法：自构建上下文反编译（sc$^2$dec）方法在没有微调的情况下重新编译 LLM 的反编译结果，以构造上下文学习对，帮助模型提高反编译性能；细粒度对齐增强（FAE）方法通过利用调试信息，在微调阶段将汇编代码与源代码在语句级别进行精确对齐，进一步提高反编译性能。集成这两种方法后，在 Decompile-Eval 基准测试上实现了约 7.35％的 Re-Exe（再执行性能）提升，建立了 55.03％的最新最佳性能水平。