Automated vulnerability detection in C source code is a long-standing challenge, as traditional static analysis tools often trade precision for scalability and struggle with complex vulnerability patterns. Recently, large language models (LLMs) have shown promising results; yet, most approaches rely on a single code re...
Francesco C. Grasso, Pietro Liguori, R. Natella et al.· 0 citations
This work compares human-written and AI-generated code at scale, characterize structural complexity and statistical naturalness, and map static-analysis findings onto Orthogonal Defect Classification for defects and the Common Weakness Enumeration for vulnerabilities, making authors and languages directly comparable.
Cristina Improta, Pietro Liguori, D. Cotroneo· 0 citations
It is shown that LLM-based fault injection extends the behavioral coverage of fixed fault models without establishing general superiority, and that practical adoption still requires controlled generation, runtime validation, system-level oracles, and reproducible experimental provenance.
G. De Rosa, Pietro Liguori, D. Cotroneo· 0 citations
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