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.
Quality assessment of pythontestsgeneratedbylargelanguagemodels.arXivpreprint
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LLMs achieve only 0-60% success when asked to contribute code to sizable open-source projects, often failing basic checks or simply repeating training data.
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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.
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Can LLMs be Effective Code Contributors? A Study on Open-source Projects
LLMs achieve only 0-60% success when asked to contribute code to sizable open-source projects, often failing basic checks or simply repeating training data.