LLM-Agent-Style Automated Usability Testing on MiniWoB++: A Reproducible Chunked Full-Run with ReAct, Plan-Execute, and Self-Reflect Policies

Automated usability testing can reduce the cost of repeatedly checking whether a web interface supports reliable and efficient task completion, but existing scripted tests are brittle and many agent evaluations report benchmark scores without translating failures into usability diagnostics. This study asks how three LLM-agent-style strategies—ReAct, Plan-Execute, and Self-Reflect—differ in effectiveness, efficiency, and failure modes when applied to MiniWoB++ tasks, and whether their logged traces can support actionable UI analysis. We conducted a controlled experimental benchmark on 130 MiniWoB++ web tasks, running each strategy once under a fixed seed with the same deterministic DOM-grounded controller, headless Chromium harness, 10-step limit, and 2.0 s episode budget, producing 390 episodes. We analyzed task success, steps, wall-clock time, interaction category, difficulty bins, and failure categories using paired per-task comparisons and descriptive aggregation. Plan-Execute achieved the highest success rate (14.6%, 19/130), compared with 10.8% (14/130) for both ReAct and Self-Reflect; its advantage was most evident in form/transaction and selection tasks, while all strategies performed similarly on simple click/button tasks and failed on drag/scroll tasks. Failure analysis showed that wrong outcomes and element-grounding errors were the dominant bottlenecks, indicating that explicit planning improves coverage only when target elements can be reliably grounded. The findings contribute a reproducible baseline and a usability-oriented failure taxonomy for automated web-agent testing, suggesting that future frameworks should prioritize semantic grounding, plan validation, richer action primitives, and designer-facing diagnostics.