Software in the wild is usually released as stripped binaries that contain no debug information (e.g., function names). This paper studies the issue of reassigning descriptive names for functions to help facilitate reverse engineering. Since the essence of this issue is a data-driven prediction task, persuasive research should be based on sufficiently large-scale and diverse data. However, prior studies can only be based on small-scale datasets because their techniques suffer from heavyweight binary analysis, making them powerless in the face of big-size and large-scale binaries.

This paper presents the Neural Function Rename Engine (NFRE), a lightweight framework for function name reassignment that utilizes both sequential and structural information of assembly code. NFRE uses fine-grained and easily acquired features to model assembly code, making it more effective and efficient than existing techniques. In addition, we construct a large-scale dataset and present two data-preprocessing approaches to help improve its usability. Benefiting from the lightweight design, NFRE can be efficiently trained on the large-scale dataset, thereby having better generalization capability for unknown functions. The comparative experiments show that NFRE outperforms two existing techniques by a relative improvement of 32% and 16%, respectively, while the time cost for binary analysis is much less.