在需求阶段测量复杂性：谱度量作为开发努力预测器
Measuring Complexity at the Requirements Stage: Spectral Metrics as Development Effort Predictors
AI总结：
本文通过自然语言处理方法提取需求规范的结构网络，利用分子整合任务作为结构同构代理，发现谱度量对整合努力的预测相关性超过0.95，揭示了谱度量在捕捉认知和努力维度上的优势。
摘要：
工程系统中的复杂性是现代开发中最持久的挑战之一，因为它导致成本超支、进度延迟和项目失败。然而，尽管已研究了架构复杂性，但嵌入在需求规范中的结构性复杂性仍缺乏理解和量化。为解决这一差距，我们基于自然语言处理方法提取文本需求的结构网络，并通过分子整合任务作为结构同构代理进行受控实验，利用分子图与需求网络的拓扑等价性，消除领域专业知识和语义模糊性的影响。结果表明，谱度量对整合努力的预测相关性超过0.95，而结构性度量的相关性超过0.89。值得注意的是，基于密度的度量无显著预测有效性。这些发现表明，由特征值导出的度量能够捕捉认知和努力维度，而更简单的连接度量无法做到这一点。因此，本研究弥合了架构复杂性分析与需求工程实践之间的关键方法学差距，为将这些度量应用于需求工程提供了验证基础，其中相似的结构性复杂性模式可能预测整合努力。
英文摘要：
Complexity in engineered systems presents one of the most persistent challenges in modern development since it is driving cost overruns, schedule delays, and outright project failures. Yet while architectural complexity has been studied, the structural complexity embedded within requirements specifications remains poorly understood and inadequately quantified. This gap is consequential: requirements fundamentally drive system design, and complexity introduced at this stage propagates through architecture, implementation, and integration. To address this gap, we build on Natural Language Processing methods that extract structural networks from textual requirements. Using these extracted structures, we conduct a controlled experiment employing molecular integration tasks as structurally isomorphic proxies for requirements integration -- leveraging the topological equivalence between molecular graphs and requirement networks while eliminating confounding factors such as domain expertise and semantic ambiguity. Our results demonstrate that spectral measures predict integration effort with correlations exceeding 0.95, while structural metrics achieve correlations above 0.89. Notably, density-based metrics show no significant predictive validity. These findings indicate that eigenvalue-derived measures capture cognitive and effort dimensions that simpler connectivity metrics cannot. As a result, this research bridges a critical methodological gap between architectural complexity analysis and requirements engineering practice, providing a validated foundation for applying these metrics to requirements engineering, where similar structural complexity patterns may predict integration effort.
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