.png){kind=icon}
.png){kind=icon}
Return
Hierarchical binary diffing with inductive code representation learning with graph sampling-and-aggregate
.png){kind=icon}
OA
AI
DOI：10.1016/j.engappai.2025.112279.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• We formulate sequential generation of instruction- to function-level embeddings for a binary program. • We use hierarchical embeddings to compute function-to-block similarity and perform code diffing. • We implement the DiffSAGE prototype and evaluate it on a large binary dataset, showing it outforms state-of-the-art.
Keywords:
Binary diffing
Word embedding
Graph embedding
Representation learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8
Papers:
5.3K
Citations:
3.5W
Organization
No organization information available