A Survey on Test Case Design Using Graph Models in Blockchain

Blockchain smart contracts have enabled decentralized applications but face over $900 million in annual losses due to vulnerabilities that cannot be patched after deployment. Graph-based testing has emerged as a promising solution, yet no standardized framework exists to compare approaches. This paper presents a systematic literature review of graph-based test case design methodologies for blockchain systems. Following a structured search of IEEE Xplore, ACM Digital Library, arXiv, and Scopus (2020–2026), we identified and analyzed 15 peer-reviewed studies. We propose a novel taxonomy classifying approaches by graph construction technique (CFG, DFG, CG, heterogeneous), testing objective (vulnerability detection, test generation, optimization), and validation methodology. Key findings: heterogeneous graph models integrating multiple relationship types outperform single-view approaches by 15–39% in F1 score; bytecode-level detection is critical for practical deployment; explainability remains a barrier to professional adoption. Major gaps identified include: absence of cross-contract interaction testing, no standardized benchmark for comparative evaluation, and lack of dynamic/temporal graph models. This survey provides researchers and practitioners with a structured foundation for selecting, evaluating, and advancing graph-based testing approaches for blockchain systems.