Tables or Sankey Diagrams? Investigating User Interaction with Different Representations of Simulation Parameters

TL;DR

The paper tackles the problem of understanding complex parameter interdependencies in configuration-heavy software (e.g., CAE) by comparing interactive Sankey diagrams against traditional spreadsheets using the PURE heuristic method with domain experts. It shows that flow-based parameter visualizations significantly reduce cognitive load ($51\%$ lower PURE scores) and interaction effort ($56\%$ fewer steps) by making parameter relationships visually explicit. The approach aligns visualization with engineers’ mental models, enabling quicker identification of critical dependencies and more efficient exploration, with implications beyond CAE to ERP, databases, and build configuration domains. The work provides practical evidence and a blueprint for deploying dependency-aware visualizations in configuration-intensive systems; the Appendix formalizes the parameter relationships, e.g., $F_g = m g$ and $x = F_g / k = m g / k$, which are later extended to $x = 2 m g / k$ in the use case.

Abstract

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.

Figures (3)

• Figure 1: Microsoft Excel UI with task 3: Change the value of a local parameter, where the global parameter was used
• Figure 2: Sankey Diagram. The links between nodes reflect how global parameters influence local parameters. The width of links changes depending on the values
• Figure 3: PURE scores. Sankey diagrams have a lower PURE score in comparison to Excel spreadsheets