DECodeT5: A Lightweight and Efficient Neural Decompiler With Assembly Semantic Assistance

Decompilation plays a critical role in firmware analysis and reverse engineering by enabling the recovery of high-level source code from binary executables. However, the existing neural decompilation models often face challenges due to the semantic gap between assembly code and high-level languages, and they typically require large-scale models that impose significant computational demands. In this article, we propose DECodeT5, a lightweight and efficient neural decompilation method for the C language. DECodeT5 builds upon the CodeT5 code generation model by integrating a pretrained assembly encoder in place of its original embedding layer. This modification allows for more effective semantic learning from assembly code, improving the model’s ability to capture the intricacies of assembly-to-source code mapping. The architecture of DECodeT5 not only accelerates convergence during end-to-end decompilation tasks but also supports rapid adaptation across different compilers and instruction set architectures (ISAs) by swapping out the encoder module as needed. Our experimental evaluation on the HumanEval and Exebench benchmarks reveals that DECodeT5 significantly improves semantic recovery accuracy, outperforming Ghidra by over 83% and LLM4Decompile by more than 43% in terms of execution recovery. Furthermore, DECodeT5 maintains a compact model size of only 360M parameters, providing inference speeds that are twice as fast as LLM4Decompile. These results underscore DECodeT5’s suitability for deployment in resource-constrained environments and its flexibility in adapting to real-world reverse engineering scenarios, offering a practical solution for modern firmware analysis and security tasks.