AI-Driven Automated Test Generation Framework for VCU: A Multidimensional Coupling Approach Integrating Requirements, Variables and Logic

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

AI-Driven Automated Test Generation Framework for VCU: A Multidimensional Coupling Approach Integrating Requirements, Variables and Logic

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

AI-Driven Automated Test Generation Framework for VCU: A Multidimensional Coupling Approach Integrating Requirements, Variables and Logic

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

World Electric Vehicle Journal

ISSN

2032-6653

e-ISSN

—

Svazek periodika

16

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

417

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105014327568