AI-Driven Automated Test Generation Framework for VCU: A Multidimensional Coupling Approach Integrating Requirements, Variables and Logic
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AS3JQSWPT" target="_blank" >RIV/00216208:11320/26:S3JQSWPT - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.3390/wevj16080417" target="_blank" >http://dx.doi.org/10.3390/wevj16080417</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/wevj16080417" target="_blank" >10.3390/wevj16080417</a>
Alternative languages
Result language
angličtina
Original language name
AI-Driven Automated Test Generation Framework for VCU: A Multidimensional Coupling Approach Integrating Requirements, Variables and Logic
Original language description
This paper proposes an AI-driven automated test generation framework for vehicle control units (VCUs), integrating natural language processing (NLP) and dynamic variable binding. To address the critical limitation of traditional AI-generated test cases lacking executable variables, the framework establishes a closed-loop transformation from requirements to executable code through a five-layer architecture: (1) structured parsing of PDF requirements using domain-adaptive prompt engineering; (2) construction of a multidimensional variable knowledge graph; (3) semantic atomic decomposition of requirements and logic expression generation; (4) dynamic visualization of cause–effect graphs; (5) path-sensitization-driven optimization of test sequences. Validated on VCU software from a leading OEM, the method achieves 97.3% variable matching accuracy and 100% test case executability, reducing invalid cases by 63% compared to conventional NLP approaches. This framework provides an explainable and traceable automated solution for intelligent vehicle software validation, significantly enhancing efficiency and reliability in automotive testing. © 2025 by the authors.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
World Electric Vehicle Journal
ISSN
2032-6653
e-ISSN
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Volume of the periodical
16
Issue of the periodical within the volume
8
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
417
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105014327568