AI 中文总结
本研究提出面向需求的标准化方法，将原始需求转换为标准化用例规格，经实验验证该方法可提升语义模糊需求的可追溯性能，同时揭示其适用边界，为可追溯性改进提供新策略。
AI 中文摘要
需求与源代码之间的可追溯性链接恢复对软件质量保证和演化分析至关重要。尽管自动化可追溯性技术已取得很大进展，但模糊的自然语言需求与精确的源代码之间存在的巨大语义差距仍然阻碍了准确的链接恢复。大多数现有方法优化了可追溯性算法，却忽略了需求描述的固有质量，这无法从根本上缩小语义差距。本研究提出一种面向需求的标准化方法，使用受控自然语言和基于大语言模型的提示工程，将原始需求分解并转换为标准化用例规格，以增强语义表示并减少语义分歧。在两个典型可追溯性框架下的四个公开数据集上进行评估，结果显示，标准化规格提升了语义模糊的原始需求的可追溯性能，但过度标准化可能会对已与代码语义高度一致的高质量需求的结果产生负面影响。这些结果验证了源端需求标准化是提升可追溯性的有效策略，并揭示了其在实际应用中的适用边界。
英文摘要
Traceability link recovery between requirements and source code is vital for software quality assurance and evolution analysis. Although automated traceability techniques have advanced greatly, the large semantic gap between vague natural-language requirements and precise source code still hinders accurate link recovery. Most existing approaches optimize traceability algorithms yet ignore the inherent quality of requirement descriptions, which prevents fundamental reduction of the semantic gap. This work proposes a requirement-oriented normalization method. Using controlled natural language and large-language-model-based prompt engineering, raw requirements are decomposed and converted into standardized use-case specifications to strengthen semantic representation and mitigate semantic divergence. Evaluated on four public datasets under two typical traceability frameworks, the normalized specifications improve tracing performance for semantically ambiguous raw requirements. However, over-normalization may degrade results for already high-quality requirements closely aligned with code semantics. The results validate source-side requirement normalization as a promising strategy for traceability improvement and reveal its applicable boundaries for practical usage.
