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Testing is the most direct and effective technique to ensure softwarequality. However, it is a burden for developers to understand thepoorly-commented tests, which are common in industry environment projects.Mobile applications (app) are GUI-intensive and event-driven, so test scriptsfocusing on GUI interactions play a more important role in mobile app testingbesides the test cases for the source code. Therefore, more attention should bepaid to the user interactions and the corresponding user event responses.However, test scripts are loosely linked to apps under test (AUT) based onwidget selectors, making it hard to map the operations to the functionalitycode of AUT. In such a situation, code understanding algorithms may loseefficacy if directly applied to mobile app test scripts.  We present a novel approach, TestIntent, to infer the intent of mobile apptest scripts. TestIntent combines the GUI image understanding and codeunderstanding technologies. The test script is transferred into an operationsequence model. For each operation, TestIntent extracts the operated widgetselector and link the selector to the UI layout structure, which stores thedetailed information of the widgets, including coordinates, type, etc. Withcode understanding technologies, TestIntent can locate response methods in thesource code. Afterwards, NLP algorithms are adopted to understand the code andgenerate descriptions. Also, TestIntent can locate widgets on the app GUIimages. Then, TestIntent can understand the widget intent with anencoder-decoder model. With the combination of the results from GUI and codeunderstanding, TestIntent generates the test intents in natural languageformat. We also conduct an empirical experiment, and the results prove theoutstanding performance of TestIntent. A user study also declares thatTestIntent can save developers' time to understand test scripts.

Automated Mobile App Test Script Intent Generation via Image and Code Understanding