Vision-Based Widget Mapping for Test Migration Across Mobile Platforms: Are We There Yet?

Automated GUI testing through the reuse of existing tests has recently gained prominence in research. Cross-platform migration of GUI tests between different platform versions of an application offers a promising opportunity for test reuse. Widget mapping, identifying similarities between source and target application widgets and connecting semantically analogous pairs, is central to these approaches. Vision-based widget mapping approaches are supposed to provide platform-agnostic solutions more suitable for cross-platform migration, considering that different platform versions frequently display strong resemblances in the appearance of their semantically similar widgets. However, the efficacy of vision-based widget mapping for cross-platform migration remains limited and the reasons remain unclear. In this paper, we present the first comprehensive investigation of vision-based widget mapping for cross-platform GUI test migration. We devote considerable effort to constructing a dataset consisting of 6,730 bi-directional mapped widget pairs across the iOS and Android platforms, and categorize the mapped widgets into eight classifications to thoroughly assess the capabilities of various approaches. We implement 89 configurations, derived from five distinct vision-based widget mapping methodologies, and evaluate their performance utilizing our dataset. Our findings reveal valuable insights that can be employed to advance vision-based widget mapping techniques: (1) The current approach exhibits potential for improvement, as certain configurations demonstrate superior performance in comparison to existing methods; (2) Some features can adversely impact the mapping, requiring more consideration; (3) A substantial proportion of mapped widgets display varying inconsistent contents in their appearance, which require more sophisticated vision algorithms.