Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Code
Large Language Models (LLMs) are increasingly changing how code is produced, but generated code still requires human review and validation before it can be adapted or integrated into real-world projects. This makes the readability of LLM-generated code a critical concern. Existing studies have mainly focused on functional correctness and task completion of LLM generated code, leaving open questions about whether it is readable, how its readability fails, and to what extent prompt design can improve it. We therefore investigate the readability of LLM-generated code. We first construct a readability assessment model that integrates textual, structural, program, and visual features. Using th...
| Authors | Hengzhi Ye, Fengyuan Ran, Weiwei Xu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 13 May 2026 |
| Last updated at source | 3 August 2026 |
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