Beyond Surveys: A Deep Analysis of Persistent Challenges in Vulnerable Code Clone Detection
Keywords:
.Abstract
This paper departs from traditional survey-based analyses to deeply explore the persistent, unresolved technical, semantic, and practical challenges in detecting vulnerable code clones. Despite significant methodological advancements in vulnerable code clone
detection ranging from textual matching and abstract syntax tree (AST) based syntactic analyses to deep learning-driven semantic approaches, numerous critical challenges remain inadequately addressed. Specifically, semantic complexity, advanced obfuscation,
computational scalability, language heterogeneity, and dataset inadequacy undermine detection reliability
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