# Cross-Language Binary-Source Code Matching with Intermediate Representations arXiv: https://arxiv.org/abs/2201.07420 ยท HTML: https://mlsanity.com/paper/2201.07420/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 53 references that are in the map. - First posted on arXiv 19 Jan 2022. ## Builds on (most cited first, 26 of 26) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015) - [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) - [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020) - [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015) - [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018) - [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018) - [Improving Automatic Source Code Summarization via Deep Reinforcement Learning](https://mlsanity.com/paper/1811.07234/) (2018) - [Tree-to-tree Neural Networks for Program Translation](https://mlsanity.com/paper/1802.03691/) (2018) - [Improved Code Summarization via a Graph Neural Network](https://mlsanity.com/paper/2004.02843/) (2020) - [Code Prediction by Feeding Trees to Transformers](https://mlsanity.com/paper/2003.13848/) (2020) - [Neural Code Comprehension: A Learnable Representation of Code Semantics](https://mlsanity.com/paper/1806.07336/) (2018) - [Pythia: AI-assisted Code Completion System](https://mlsanity.com/paper/1912.00742/) (2019) - [Deep API Learning](https://mlsanity.com/paper/1605.08535/) (2016) - [Multi-Modal Attention Network Learning for Semantic Source Code Retrieval](https://mlsanity.com/paper/1909.13516/) (2019) - [SourcererCC: Scaling Code Clone Detection to Big Code](https://mlsanity.com/paper/1512.06448/) (2015) - [How could Neural Networks understand Programs?](https://mlsanity.com/paper/2105.04297/) (2021) - [A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning](https://mlsanity.com/paper/1909.06983/) (2019) - [BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet](https://mlsanity.com/paper/1705.09864/) (2017) - [Convolutional Neural Networks over Tree Structures for Programming Language Processing](https://mlsanity.com/paper/1409.5718/) (2014) - [DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning](https://mlsanity.com/paper/1704.07734/) (2017) - [SAR: Learning Cross-Language API Mappings with Little Knowledge](https://mlsanity.com/paper/1906.03835/) (2019) - [BinPro: A Tool for Binary Source Code Provenance](https://mlsanity.com/paper/1711.00830/) (2017) - [NaturalCC: A Toolkit to Naturalize the Source Code Corpus](https://mlsanity.com/paper/2012.03225/) (2020) ## Built on by (most cited first, 13 of 13) - [Meta Large Language Model Compiler: Foundation Models of Compiler Optimization](https://mlsanity.com/paper/2407.02524/) (2024) - [SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly](https://mlsanity.com/paper/2305.12520/) (2023) - [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023) - [Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps](https://mlsanity.com/paper/2309.04142/) (2023) - [CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale](https://mlsanity.com/paper/2502.16645/) (2025) - [Automating the Correctness Assessment of AI-generated Code for Security Contexts](https://mlsanity.com/paper/2310.18834/) (2023) - [A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks](https://mlsanity.com/paper/2403.10646/) (2024) - [FASER: Binary Code Similarity Search through the use of Intermediate Representations](https://mlsanity.com/paper/2310.03605/) (2023) - [NeuDep: Neural Binary Memory Dependence Analysis](https://mlsanity.com/paper/2210.02853/) (2022) - [A Picture is Worth 500 Labels: A Case Study of Demographic Disparities in Local Machine Learning Models for Instagram and TikTok](https://mlsanity.com/paper/2403.19717/) (2024) - [InstMeter: An Instruction-Level Method to Predict Energy and Latency of DL Model Inference on MCUs](https://mlsanity.com/paper/2603.04134/) (2026) - [Can Large Language Models Understand Intermediate Representations in Compilers?](https://mlsanity.com/paper/2502.06854/) (2025) - [Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity](https://mlsanity.com/paper/2509.23449/) (2025) ## Abstract (arXiv) Binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment. Currently, several approaches have been proposed for binary-source code matching by jointly learning the embeddings of binary code and source code in a common vector space. Despite much effort, existing approaches target on matching the binary code and source code written in a single programming language. However, in practice, software applications are often written in different programming languages to cater for different requirements and computing platforms. Matching binary and source code across programming languages introduces additional challenges when maintaining multi-language and multi-platform applications. To this end, this paper formulates the problem of cross-language binary-source code matching, and develops a new dataset for this new problem. We present a novel approach XLIR, which is a Transformer-based neural network by learning the intermediate representations for both binary and source code. To validate the effectiveness of XLIR, comprehensive experiments are conducted on two tasks of cross-language binary-source code matching, and cross-language source-source code matching, on top of our curated dataset. Experimental results and analysis show that our proposed XLIR with intermediate representations significantly outperforms other state-of-the-art models in both of the two tasks.