Pre-Training Representations of Binary Code Using Contrastive Learning

TL;DR

ContraBin is a novel contrastive learning framework that integrates source code, comments, and binary code to improve binary analysis and comprehension tasks, revealing insights about comment quality and significantly enhancing downstream task performance.

Contribution

Introduces ContraBin, the first model combining source code, comments, and binary code for contrastive learning in binary analysis, with new methods for integrating diverse representations.

Findings

Synthetic comments improve binary comprehension performance.

Human-written comments can introduce noise and reduce accuracy.

ContraBin outperforms existing methods on multiple binary analysis tasks.

Abstract

Binary code analysis and comprehension is critical to applications in reverse engineering and computer security tasks where source code is not available. Unfortunately, unlike source code, binary code lacks semantics and is more difficult for human engineers to understand and analyze. In this paper, we present ContraBin, a contrastive learning technique that integrates source code and comment information along with binaries to create an embedding capable of aiding binary analysis and comprehension tasks. Specifically, we present three components in ContraBin: (1) a primary contrastive learning method for initial pre-training, (2) a simplex interpolation method to integrate source code, comments, and binary code, and (3) an intermediate representation learning algorithm to train a binary code embedding. We further analyze the impact of human-written and synthetic comments on binary code comprehension tasks, revealing a significant performance disparity. While synthetic comments provide substantial benefits, human-written comments are found to introduce noise, even resulting in performance drops compared to using no comments. These findings reshape the narrative around the role of comment types in binary code analysis. We evaluate the effectiveness of ContraBin through four indicative downstream tasks related to binary code: algorithmic functionality classification, function name recovery, code summarization, and reverse engineering. The results show that ContraBin considerably improves performance on all four tasks, measured by accuracy, mean of average precision, and BLEU scores as appropriate. ContraBin is the first language representation model to incorporate source code, binary code, and comments into contrastive code representation learning and is intended to contribute to the field of binary code analysis. The dataset used in this study is available for further research.