Automating a Complete Software Test Process Using LLMs: An Automotive Case Study

Vehicle API testing verifies whether the interactions between a vehicle's internal systems and external applications meet expectations, ensuring that users can access and control various vehicle functions and data. However, this task is inherently complex, requiring the alignment and coordinati...

Full description

Saved in:
Bibliographic Details
Published inProceedings / International Conference on Software Engineering pp. 373 - 384
Main Authors Wang, Shuai, Yu, Yinan, Feldt, Robert, Parthasarathy, Dhasarathy
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.04.2025
Subjects
Automation
Automotive engineering
large language model
Large language models
Manuals
Software engineering
Software testing
test automation
Testing
Transforms
vehicle API testing
Web servers
Writing
Online AccessGet full text

Cover

More Information
Summary:Vehicle API testing verifies whether the interactions between a vehicle's internal systems and external applications meet expectations, ensuring that users can access and control various vehicle functions and data. However, this task is inherently complex, requiring the alignment and coordination of API systems, communication protocols, and even vehicle simulation systems to develop valid test cases. In practical industrial scenarios, inconsistencies, ambiguities, and interde-pendencies across various documents and system specifications pose significant challenges. This paper presents a system designed for the automated testing of in-vehicle APIs. By clearly defining and segmenting the testing process, we enable Large Language Models (LLMs) to focus on specific tasks, ensuring a stable and controlled testing workflow. Experiments conducted on over 100 APIs demonstrate that our system effectively automates vehicle API testing. The results also confirm that LLMs can efficiently handle mundane tasks requiring human judgment, making them suitable for complete automation in similar industrial contexts.
ISSN:0270-5257
1558-1225
DOI:10.1109/ICSE55347.2025.00211