AURA: ADAPTIVE UI RECOVERY ARCHITECTURE FOR ANDROID TEST AUTOMATION - M Ilham Yusuf Gumai; Suhendro Yusuf Irianto; RZ Abdul Aziz; Rahmalia Syahputri (Bulletin of Network Engineer and Informatics, 2026)
User interface (UI) test automation on Android frequently breaks when developers rename element attributes during refactoring, rendering previously valid locators unresolvable and imposing significant maintenance overhead. Existing self-healing approaches predominantly target web DOM and lack post-action validation, risking false healing where a wrong element is silently accepted. This study introduces AURA, a runtime self-healing layer for Appium-WebdriverIO that chains five deterministic recovery strategies, a widget-family post-action validator, and an optional machine-learning reranker. A controlled benchmark comprising 490 refactoring scenarios across five synthetic Android applications and six mutator types demonstrates that AURA achieves a 99.39% correct action rate with only 0.61% false-healing rate, significantly outperforming the adapted Similo baseline (95.71% / 4.29%) at p < 0.0001 (McNemar exact test). External validation on six production Google Android applications (130 scenarios) confirms a 100% correct rate with a bounds-IoU enhanced validator. Cache learning reduces per-find latency by 95.1% from the second session onward.