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Intelligent Software Testing Platform Selection and Knowledge Graph-Driven Test Case Generation

delete2026-01-01
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PRE
AI
L
Ling Peng
Y
Yu Shrike Zhang *
DOI：10.1007/978-981-95-5294-8_12delete
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Abstract

Abstract

En 中文
This study addresses the critical challenge of selecting appropriate software testing platforms and optimizing test case generation in AI-era vocational education. We propose a novel framework that integrates knowledge graph technology with mainstream testing platforms to increase automated test case generation efficiency. The methodology combines multimodal knowledge graph construction (entity recognition accuracy: 92.7%, relationship extraction F1 score: 89.3%) with AI-powered testing tools, implementing dynamic knowledge updating through continuous integration mechanisms. The experimental results on three industrial-standard testing datasets demonstrate a 38% improvement in test coverage and a 45% reduction in false positives compared with traditional methods. The proposed system architecture enables semantic-aware test scenario generation through ontology-driven knowledge mapping, which is particularly effective in complex web application testing scenarios. This research contributes to 1) a knowledge graph-enhanced test automation framework; 2) a multimodal educational resource integration methodology; and 3) an industry-academia collaborative validation mechanism. Practical applications show a 27% increase in student competency assessment scores through our platform-embedded teaching system.
Keywords:
Software Testing Education
Knowledge Graph
Test Case Generation
AI-Driven Testing
Educational Technology

Journal

C
COMPUTER APPLICATIONS, CCF NCCA 2025
IF:
0
Papers:
35
Citations:
0

Organization

No organization information available