用ISO标准优化非功能需求描述，能提升代码质量但未必提高正确性。
Does ISO-Grounded NFR Specification Improve LLM Code Generation? A Comparison of Rich and Structured Interventions against a Natural-Language Baseline
- 将NFR规范基于ISO/IEC 25010模型，用自然语言或结构化格式表达
- 代码可读性密度下降（如性能项从0.88降至0.69），错误处理测试通过率反降
- 内容比格式更重要，自然语言与JSON效果接近，适合注重质量的开发者
在大模型代码生成中，非功能需求（NFR）常以简短单行语句描述。本文研究将这些需求基于ISO/IEC 25010质量模型进行丰富化表达（自然语言版或结构化JSON版），相比传统的简洁描述（NL-simple）是否能提升代码质量。在HumanEval和HumanEval-ET数据集上，对性能、错误处理、代码异味、可读性四个NFR进行十种提示变体测试，固定模型版本并采用配对非参数分析。主要发现：基于ISO的丰富化描述显著降低静态质量指标中的不可读密度（如性能项由0.88降至0.69），减少对提示措辞的敏感性，但未稳定提升功能性正确性；错误处理任务的扩展测试通过率反而下降，表明防御性编程与精确输出评测存在矛盾。次要发现：当内容一致时，自然语言与结构化格式在正确性上差异极小（|delta| ≤ 0.023），说明语义内容比格式形式更关键。实践建议：应优先投入标准化内容构建，而非关注序列化形式。完整可复现代码包已提供。
原文摘要 · Abstract (English)
In LLM-based code generation, Non-Functional Requirements (NFRs) are often specified as terse one-line phrases. We ask whether grounding those specifications in ISO/IEC 25010 Quality Model, either as rich natural-language prose (NL-rich) or as structured JSON (Structured), improves code generated on HumanEval/HumanEval-ET compared to a RobuNFR-style one-line baseline (NL-simple). We evaluate four NFRs (performance, error handling, code smell, readability) with ten prompt variations per condition under a fixed model snapshot and paired non-parametric analysis. Primary finding: ISO-grounded enrichment improves static quality proxies (unreadability density falls across all four NFRs (e.g., Performance 0.88 -> 0.69 for NL-rich)) and reduces sensitivity to prompt wording, but does not reliably improve functional correctness; for error handling, extended-test pass rate decreases, suggesting tension between defensive coding patterns and exact-output benchmarks. Secondary finding: when ISO content is held constant, NL-rich and Structured differ negligibly in correctness (|delta| <= 0.023), indicating that semantic content matters more than JSON-vs-prose format. Practitioners should invest in standard-grounded NFR content rather than serialization form. A fully traceable replication package is provided.
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