# Leveraging Artificial Intelligence on Binary Code Comprehension

arXiv: https://arxiv.org/abs/2210.05103 · HTML: https://mlsanity.com/paper/2210.05103/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [A Transformer-based Approach for Source Code Summarization](https://mlsanity.com/paper/2005.00653/) (2020)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [jTrans: Jump-Aware Transformer for Binary Code Similarity](https://mlsanity.com/paper/2205.12713/) (2022)
- [A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challenges](https://mlsanity.com/paper/2012.07919/) (2020)
- [TreeCaps: Tree-Based Capsule Networks for Source Code Processing](https://mlsanity.com/paper/2009.09777/) (2020)
- [Ways of Applying Artificial Intelligence in Software Engineering](https://mlsanity.com/paper/1802.02033/) (2018)
- [Automated Transpilation of Imperative to Functional Code using Neural-Guided Program Synthesis (Extended Version)](https://mlsanity.com/paper/2203.09452/) (2022)
- [Unleashing the Hidden Power of Compiler Optimization on Binary Code Difference: An Empirical Study](https://mlsanity.com/paper/2103.12357/) (2021)

## Built on by (most cited first, 3 of 3)

- [Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generation](https://mlsanity.com/paper/2501.16692/) (2025)
- [CONSTRUCTA: Automating Commercial Construction Schedules in Fabrication Facilities with Large Language Models](https://mlsanity.com/paper/2502.12066/) (2025)
- [SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair](https://mlsanity.com/paper/2604.17184/) (2026)

## Abstract (arXiv)

Understanding binary code is an essential but complex software engineering task for reverse engineering, malware analysis, and compiler optimization. Unlike source code, binary code has limited semantic information, which makes it challenging for human comprehension. At the same time, compiling source to binary code, or transpiling among different programming languages (PLs) can provide a way to introduce external knowledge into binary comprehension. We propose to develop Artificial Intelligence (AI) models that aid human comprehension of binary code. Specifically, we propose to incorporate domain knowledge from large corpora of source code (e.g., variable names, comments) to build AI models that capture a generalizable representation of binary code. Lastly, we will investigate metrics to assess the performance of models that apply to binary code by using human studies of comprehension.