Unsupervised Binary Code Translation with Application to Code Clone Detection and Vulnerability Discovery (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study shows that binary code analysis is a key problem in software security research. |
| Approach: | They propose to apply Neural Machine Translation to binary code analysis . they translate a binary in a low-resource ISA and train a model on the high-resourced ISA . |
| Outcome: | The proposed model can be used to analyze binary code across ISAs using a high-resource ISA. |
Similar Papers
Learning Cross-Architecture Instruction Embeddings for Binary Code Analysis in Low-Resource Architectures (2024.findings-naacl)
Copied to clipboard
| Challenge: | Applying deep learning to binary code analysis has drawn great attention because of its notable performance. |
| Approach: | They propose to learn cross-architecture instruction embeddings where semantically-similar instructions have close embeddements in a shared space. |
| Outcome: | The proposed approach generates high-quality CAIE with good transferability on four ISAs. |
FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Using deep learning to detect malware has attracted great attention due to its notable performance. |
| Approach: | a new approach uses Neural Machine Translation and Normalizing Flows to apply deep learning to malware detection. |
| Outcome: | The proposed approach reduces the burden of data collection by enabling malware detection across multiple ISAs. |
Disentangled Code Representation Learning for Multiple Programming Languages (2021.findings-acl)
Copied to clipboard
| Challenge: | Developing effective distributed representations of source code is challenging . current code embedding approaches that represent the semantic and syntax of code are less interpretable . |
| Approach: | They propose a disentangled code representation learning approach to separate the semantic from the syntax of source code under a multi-programming-language setting. |
| Outcome: | The proposed approach achieves better interpretability and generalizability over existing methods. |
Unifying Cross-Lingual Transfer across Scenarios of Resource Scarcity (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to deal with resource scarcity have not been developed to deal effectively with the problem. |
| Approach: | They propose to use a set of tools to harness data from one or more high-resource "source" languages to compensate for a shortage of data in low-resourced "target" languages. |
| Outcome: | The proposed technique can be easily adapted to unseen languages, extending the range of the proposed technique and translation-based transfer more broadly. |
Addressing Leakage in Self-Supervised Contextualized Code Retrieval (2022.coling-1)
Copied to clipboard
| Challenge: | a recent study addresses the use of contextualized code retrieval to fill gaps in a partial input program. |
| Approach: | They propose a self-supervised approach to contextualized code retrieval . they propose mutual identifier masking, dedentation, and the selection of syntax-aligned targets . |
| Outcome: | The proposed approach improves retrieval substantially and yields state-of-the-art results for code clone and defect detection. |
CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing code translation datasets focus on a single pair of programming languages . early software systems are developed using programming languages such as Fortran and COBOL . |
| Approach: | They propose a large-scale comprehensive benchmark that supports the largest variety of programming languages for code translation. |
| Outcome: | The proposed framework supports translations between multiple programming languages and a cross-framework dataset for deep learning code across different frameworks. |
CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search (2025.naacl-srw)
Copied to clipboard
Nikita Sorokin, Tikhonov Anton, Dmitry Abulkhanov, Ivan Sedykh, Irina Piontkovskaya, Valentin Malykh
| Challenge: | clone detection is crucial in software development for identifying semantically similar code . clones can be found in the same language code snippets, but there is little research on multilingual clonage detection. |
| Approach: | They propose a novel training procedure leveraging cross-lingual similarity to train language models on source code in various programming languages. |
| Outcome: | The proposed method achieves state-of-the-art on C++ and Python clone detection benchmarks with comparable performance on decoder-based models. |
The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Open-source dataset of code-text pairs for training large language models to understand code is outperforms other datasets for code generation and understanding tasks. |
| Approach: | They propose to extract high-quality code-text pairs from a dataset of 43 million pairs . they use rules and deep learning to ensure that the code-sampled samples contain high-quality pairs a . |
| Outcome: | The Vault dataset outperforms existing models on common coding tasks . authors hope the results will propel AI research and software development forward . |
Language Agnostic Code Embeddings (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent studies show that code language models have strong cross-lingual traits, but their multilingual representations can be dissected into a language-specific syntax component and a semantic component. |
| Approach: | They propose to isolate and eliminate language-specific components from multilingual code embeddings to improve downstream code retrieval tasks. |
| Outcome: | The proposed model improves retrieval tasks by removing language-specific components . the proposed model can be used to perform a variety of code generation tasks . |
CodeT5+: Open Code Large Language Models for Code Understanding and Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing code LLMs adopt a specific architecture or rely on a unified encoder-decoder network for downstream tasks, lacking flexibility to operate in the optimal architecture for a particular task. |
| Approach: | They propose to initialize code LLMs with frozen off-the-shelf LLM and explore instruction-tuning to align with natural language instructions. |
| Outcome: | The proposed model outperforms open-source LLMs on 20 code-related benchmarks. |