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 conducted 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.

翻译：在工程系统中，复杂性是现代开发过程中最持久的挑战之一，因为它直接导致成本超支、进度延误乃至项目彻底失败。尽管架构复杂性已得到广泛研究，但需求规格说明中蕴含的结构复杂性仍缺乏深入理解与有效量化。这一空白具有重要影响：需求从根本上驱动系统设计，此阶段引入的复杂性会贯穿架构、实现与集成全过程。为填补这一空白，本研究基于从文本需求中提取结构网络的自然语言处理方法展开。利用提取的网络结构，我们通过分子集成任务作为需求集成的结构同构代理开展受控实验——该方法既利用了分子图与需求网络间的拓扑等价性，又消除了领域专业知识与语义歧义等混杂因素。实验结果表明：谱度量对集成工作量的预测相关性超过0.95，结构度量相关性亦达0.89以上；值得注意的是，基于密度的度量未显示出显著预测效度。这些发现表明，特征值导出的度量能够捕捉简单连接性度量无法涵盖的认知与工作量维度。因此，本研究在架构复杂性分析与需求工程实践之间架起了关键的方法论桥梁，为将此类度量应用于需求工程提供了验证基础——在该领域中，类似的结构复杂性模式同样可能预测集成工作量。