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CodeEnhancer: LLM-generated Python code enhancement through SAST integration and fine-tuning
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DOI：10.1016/j.knosys.2026.115925.png)
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Abstract
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• Combines language models with SAST Tools to enhance Syntax, security and functional correctness Python code. • First approach to address syntax, security, and functional correctness in LLM-generated code. • Automated feedback and learning process helps LLMs generate more secure, correct code. • Fine-tuning on framework-refined code leads to better security than training on expert-written code. • Scalable approach enables robust and trustworthy AI-assisted code generation and refinement with minimal manual effort.
Keywords:
Large Language Models (LLMs)
Syntax checking
Vulnerability detection
Functional correctness
Static application security testing
LLM fine-tuning
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