Room for Improvement: Dashboard Design Approaches to Facilitate Smart Home Fault Diagnosis

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Smart homes are increasingly complex ecosystems of interconnected devices and automations, yet their opaque nature makes fault diagnosis difficult for end-users. We investigated how dashboard design affects diagnostic performance by comparing three interfaces: functional (device-centric control), spatial (physical layout), and relational (dependency graphs). In a controlled study with 36 participants diagnosing authentic faults across three complexity levels, structural interfaces — spatial and relational — significantly improved diagnostic accuracy compared to the functional baseline without increasing cognitive workload or time. Critically, optimal representation varied by problem type: spatial layouts excelled for localised state-awareness problems, dependency graphs excelled for complex logical dependencies, and both outperformed functional views for temporal reasoning. However, participants perceived worse performance when using dependency graphs despite diagnosing more accurately, revealing a transparency-complexity trade-off. These findings demonstrate that effective diagnostic support requires matching representational strategy to problem characteristics and motivate adaptive dashboard designs providing multiple views with progressive disclosure and user-controlled mode switching.