Understanding complex parameter dependencies is critical for effective configuration and maintenance of software systems across diverse domains - from Computer-Aided Engineering (CAE) to cloud infrastructure and database management. However, legacy tabular interfaces create a major bottleneck: engineers cannot easily comprehend how parameters relate across the system, leading to inefficient workflows, costly configuration errors, and reduced system trust - a fundamental program comprehension challenge in configuration-intensive software. This research evaluates whether interactive Sankey diagrams can improve comprehension of parameter dependencies compared to traditional spreadsheet interfaces. We employed a heuristic evaluation using the PURE method with three expert evaluators (UX design, simulation, and software development specialists) to compare a Sankey-based prototype to traditional tabular representations for core engineering tasks. Our key contribution demonstrates that flow-based parameter visualizations significantly reduce cognitive load (51% lower PURE scores) and interaction complexity (56% fewer steps) compared to traditional tables, while making parameter dependencies immediately visible rather than requiring mental reconstruction. By explicitly visualizing parameter relationships, Sankey diagrams address a core software visualization challenge: helping users comprehend complex system configurations without requiring deep tool-specific knowledge. While demonstrated through CAE software, this research contributes to program comprehension and software visualization by showing that dependency-aware visualizations can significantly improve understanding of configuration-intensive systems. The findings have implications for any software domain where comprehending complex parameter relationships is essential for effective system use and maintenance.

翻译：理解复杂的参数依赖关系对于从计算机辅助工程（CAE）到云基础设施和数据库管理等不同领域的软件系统的有效配置与维护至关重要。然而，传统的表格界面构成了一个主要瓶颈：工程师难以轻松理解参数在整个系统中的关联，导致工作流程低效、配置错误成本高昂以及系统信任度降低——这是配置密集型软件中一个根本性的程序理解挑战。本研究评估了交互式桑基图相较于传统的电子表格界面，是否能改善对参数依赖关系的理解。我们采用PURE方法进行启发式评估，邀请三位专家评估员（用户体验设计、仿真和软件开发专家）比较基于桑基图的原型与传统表格表示在核心工程任务中的表现。我们的核心贡献表明，与传统的表格相比，基于流的参数可视化能显著降低认知负荷（PURE分数降低51%）和交互复杂度（步骤减少56%），同时使参数依赖关系立即可见，而无需在脑海中重构。通过显式可视化参数关系，桑基图解决了一个核心的软件可视化挑战：帮助用户理解复杂的系统配置，而无需深厚的工具特定知识。虽然本研究通过CAE软件进行演示，但它通过证明依赖感知的可视化能显著提升对配置密集型系统的理解，为程序理解和软件可视化领域做出了贡献。这些发现对任何需要理解复杂参数关系以实现有效系统使用和维护的软件领域都具有启示意义。