Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
Large language models (LLMs) frequently generate defective outputs in code generation tasks, ranging from logical bugs to security vulnerabilities. While these generation failures are often treated as model-level limitations, empirical evidence increasingly traces their root causes to imperfections within the training corpora. Yet, the specific mechanisms linking training data quality issues to generated code quality issues remain largely unmapped. This paper presents a systematic literature review of 114 primary studies to investigate how training data quality issues propagate into code generation. We establish a unified taxonomy that categorizes generated code quality issues across nine...
| Authors | Kaifeng He, Xiaojun Zhang, Peiliang Cai et al. |
|---|---|
| Primary category | cs.SE |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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