CodeT5 Fine-Tuning on Syntactically Perturbed Code for Cross-Language Migration Performance

This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How does fine-tuning CodeT5 on syntactically perturbed code datasets impact Pass@K performance in cross-language migration tasks compared to standard fine-tuning. Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks, known as Code LLMs, particularly in code generation that generates source code with LLM from natural language descriptions. This burgeoning field has captured significant. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does fine-tuning CodeT5 on syntactically perturbed code datasets impact Pass@K performance in cross-language migration tasks compared to standard fine-tuning?

Autonomous literature synthesis. Automated review score: 8.0/10. Full text and citation available at Assignee Research.