asmsense Dataset Released: 228k Stripped x86-64 Functions with Source-Aware Labels

Summary:The asmsense project has released a dataset of 228,177 stripped x86-64 functions, each annotated with source-aware labels. Compiled from 341 open-source C projects, the dataset supports multiple optimization levels (-O0 to -Os) and includes English and Turkish descriptions, with Chinese planned for the future. This resource aims to assist developers in naming and describing binary functions, with applications in reverse engineering, code analysis, and AI-assisted development.

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Project Background and Objectives

asmsense is an open-source dataset and lightweight model (based on Qwen3-8B + LoRA) designed to assist in naming and describing binary functions. The project, led by Reddit user krxi, aims to address the challenge of inaccurate function naming in reverse engineering and code analysis.

Dataset Features

1. Data Scale and Source: The dataset contains 228,177 x86-64 functions from 341 open-source C projects, covering multiple optimization levels (-O0 to -Os).
2. Annotation Quality: Each function is annotated with source-aware labels, ensuring accuracy and reliability.
3. Multilingual Support: Currently, the dataset includes English and Turkish descriptions, with Chinese planned for the future.
4. Scalability: The project plans to add support for ARM64, RISC-V, MIPS, and other architectures, as well as compile in GCC/ELF environments.

Technical Mechanisms and Engineering Challenges

1. Data Collection and Processing: The project compiles open-source C projects to ensure each function has a real name and source code. This method, though time-consuming, guarantees data quality and consistency.
2. Model Training and Inference: asmsense uses the Qwen3-8B model fine-tuned with LoRA for function naming and description tasks. The introduction of LoRA reduces fine-tuning costs while maintaining model performance.
3. Multilingual Support: To support multilingual descriptions, the project needs to handle grammatical and semantic differences across languages, increasing the complexity of model training.

Future Plans and Challenges

1. Expanding Architecture Support: The project plans to add support for ARM64, RISC-V, MIPS, and other architectures, which will involve more compilation environments and data processing work.
2. Automated Evaluation: Currently, model evaluation relies on manual checks. Future plans include introducing automated evaluation mechanisms, such as using RL environments for reinforcement learning training to improve model performance.
3. Cross-Platform Compatibility: The project plans to compile in GCC/ELF environments, which will involve more cross-platform compatibility issues and require further optimization of the compilation process.

Developer Recommendations

1. Data Preprocessing: When using the asmsense dataset, it is recommended to preprocess the data, such as removing redundant data and standardizing function names, to improve model training efficiency.
2. Model Fine-Tuning: Developers can fine-tune the Qwen3-8B model according to their needs to adapt to specific application scenarios.
3. Multilingual Support: For applications requiring multilingual support, it is recommended to include more language descriptions in the training data to improve the model's generalization ability.

Conclusion

The release of asmsense provides a valuable resource for reverse engineering, code analysis, and AI-assisted development. Its open-source nature allows more developers to participate, jointly advancing technological progress and development.

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Source: Reddit r/MachineLearning (2026-10-18)

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Tags: #asmsense Follow #open-source dataset Follow #reverse engineering Follow #AI-assisted development Follow #Qwen3-8B Follow