Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.07371 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916890447708160 |
|---|---|
| author | Zhong, Yi Liu, Hongchao ZHao, Di |
| author_facet | Zhong, Yi Liu, Hongchao ZHao, Di |
| contents | As the complexity of software systems continues to increase, the demand for automated testing and maintenance tools is growing exponentially. To meet this urgent need, we propose a new assertion generation method based on Hardware Description Language (HDL). This method combines a lightweight, parameter-adjustable large language model (LLM) with the Unsloth platform to automatically generate test cases, thereby significantly reducing training costs without sacrificing accuracy or generalization performance. Empirical evaluation shows that our method can efficiently generate assertions that strictly conform to the hardware logic. This framework provides a robust and flexible solution to modern software testing and maintenance challenges. https://github.com/liusu-orange/AutoAssert-1 and https://gitee.com/OpenBPU/auto-assert1 are the locations of the source code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07371 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoAssert 1: A LoRA Fine-Tuned LLM Model for Efficient Automated Assertion Generation Zhong, Yi Liu, Hongchao ZHao, Di Software Engineering Artificial Intelligence As the complexity of software systems continues to increase, the demand for automated testing and maintenance tools is growing exponentially. To meet this urgent need, we propose a new assertion generation method based on Hardware Description Language (HDL). This method combines a lightweight, parameter-adjustable large language model (LLM) with the Unsloth platform to automatically generate test cases, thereby significantly reducing training costs without sacrificing accuracy or generalization performance. Empirical evaluation shows that our method can efficiently generate assertions that strictly conform to the hardware logic. This framework provides a robust and flexible solution to modern software testing and maintenance challenges. https://github.com/liusu-orange/AutoAssert-1 and https://gitee.com/OpenBPU/auto-assert1 are the locations of the source code. |
| title | AutoAssert 1: A LoRA Fine-Tuned LLM Model for Efficient Automated Assertion Generation |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2508.07371 |