Fine-grained Testing for Autonomous Driving Software: a Study on Autoware with LLM-driven Unit Testing
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912191960055808 |
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| author | Wang, Wenhan Xie, Xuan Huang, Yuheng Wang, Renzhi Chen, An Ran Ma, Lei |
| author_facet | Wang, Wenhan Xie, Xuan Huang, Yuheng Wang, Renzhi Chen, An Ran Ma, Lei |
| contents | Testing autonomous driving systems (ADS) is critical to ensuring their reliability and safety. Existing ADS testing works focuses on designing scenarios to evaluate system-level behaviors, while fine-grained testing of ADS source code has received comparatively little attention. To address this gap, we present the first study on testing, specifically unit testing, for ADS source code. Our study focuses on an industrial ADS framework, Autoware. We analyze both human-written test cases and those generated by large language models (LLMs). Our findings reveal that human-written test cases in Autoware exhibit limited test coverage, and significant challenges remain in applying LLM-generated tests for Autoware unit testing. To overcome these challenges, we propose AwTest-LLM, a novel approach to enhance test coverage and improve test case pass rates across Autoware packages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09866 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fine-grained Testing for Autonomous Driving Software: a Study on Autoware with LLM-driven Unit Testing Wang, Wenhan Xie, Xuan Huang, Yuheng Wang, Renzhi Chen, An Ran Ma, Lei Software Engineering Testing autonomous driving systems (ADS) is critical to ensuring their reliability and safety. Existing ADS testing works focuses on designing scenarios to evaluate system-level behaviors, while fine-grained testing of ADS source code has received comparatively little attention. To address this gap, we present the first study on testing, specifically unit testing, for ADS source code. Our study focuses on an industrial ADS framework, Autoware. We analyze both human-written test cases and those generated by large language models (LLMs). Our findings reveal that human-written test cases in Autoware exhibit limited test coverage, and significant challenges remain in applying LLM-generated tests for Autoware unit testing. To overcome these challenges, we propose AwTest-LLM, a novel approach to enhance test coverage and improve test case pass rates across Autoware packages. |
| title | Fine-grained Testing for Autonomous Driving Software: a Study on Autoware with LLM-driven Unit Testing |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2501.09866 |