Comprehensive Semantic Repair of Obsolete GUI Test Scripts for Mobile Applications

Graphical User Interface (GUI) testing is widely used to test mobile apps. As mobile apps are frequently updated and need repeated testing, to reduce the test cost, their test cases are often coded as scripts to enable automated execution using test harnesses/tools. When those mobile apps evolve, many of the test scripts, however, may become broken due to changes made to the app GUIs. While it is desirable that the broken scripts get repaired, doing it manually can be preventively expensive if the number of tests need repairing is large. We propose in this paper a novel approach named METER to repairing broken GUI test scripts automatically when mobile apps evolve. METER leverages computer vision techniques to infer GUI changes between two versions of a mobile app and uses the inferred changes to guide the repair of GUI test scripts. Since METER only relies on screenshots to repair GUI tests, it is applicable to apps targeting open or closed source mobile platforms. In experiments conducted on 22 Android apps and 6 iOS apps, repairs produced by METER helped preserve 63.7% and 38.8% of all the test actions broken by the GUI changes, respectively.