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Automated GUI testing is widely used to help ensure the quality of mobileapps. However, many GUIs require appropriate text inputs to proceed to the nextpage which remains a prominent obstacle for testing coverage. Considering thediversity and semantic requirement of valid inputs (e.g., flight departure,movie name), it is challenging to automate the text input generation. Inspiredby the fact that the pre-trained Large Language Model (LLM) has madeoutstanding progress in text generation, we propose an approach named QTypistbased on LLM for intelligently generating semantic input text according to theGUI context. To boost the performance of LLM in the mobile testing scenario, wedevelop a prompt-based data construction and tuning method which automaticallyextracts the prompts and answers for model tuning. We evaluate QTypist on 106apps from Google Play and the result shows that the passing rate of QTypist is87%, which is 93% higher than the best baseline. We also integrate QTypist withthe automated GUI testing tools and it can cover 42% more app activities, 52%more pages, and subsequently help reveal 122% more bugs compared with the rawtool.

Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testing