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.
Bugs in large language models generated code: An empirical study
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Post-release defects concentrate in older, frequently modified high-churn components and require longer and more complex fixes than pre-release defects.
A PPO agent with hybrid actions and test-driven rewards optimizes prompts for code LLMs, raising strict Pass@1 scores on MBPP+, HumanEval+, and APPS over prior methods.
Locally deployed LLMs achieve 43-45% accuracy on Python bug detection but frequently produce only partial identifications of problematic code regions.
citing papers explorer
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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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What Makes Software Bugs Escape Testing? Evidence from a Large-Scale Empirical Study
Post-release defects concentrate in older, frequently modified high-churn components and require longer and more complex fixes than pre-release defects.
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Prompt Optimization for LLM Code Generation via Reinforcement Learning
A PPO agent with hybrid actions and test-driven rewards optimizes prompts for code LLMs, raising strict Pass@1 scores on MBPP+, HumanEval+, and APPS over prior methods.
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An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code
Locally deployed LLMs achieve 43-45% accuracy on Python bug detection but frequently produce only partial identifications of problematic code regions.