Analysis of GitHub commits shows developers mostly accept LLM refactoring suggestions without changes, with modifications clustering into five patterns based on activity, prompt, and response validity.
Refining ChatGPT- Generated Code: Characterizing and Mitigating Code Quality Issues
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A review of 114 studies creates taxonomies for code and data quality issues, formalizes 18 propagation mechanisms from training data defects to LLM-generated code defects, and synthesizes detection and mitigation techniques.
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Patterns of Developer Adoption of LLM-Generated Code Refactoring Suggestions
Analysis of GitHub commits shows developers mostly accept LLM refactoring suggestions without changes, with modifications clustering into five patterns based on activity, prompt, and response validity.
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Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
A review of 114 studies creates taxonomies for code and data quality issues, formalizes 18 propagation mechanisms from training data defects to LLM-generated code defects, and synthesizes detection and mitigation techniques.