Leveraging LLMs for Generation of Unusual Text Inputs in Mobile App Tests: Discussion and Validity

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Leveraging LLMs for Generation of Unusual Text Inputs in Mobile App Tests: Discussion and Validity
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Using InputBlaster, a novel approach in leveraging LLMs for automated generation of diverse text inputs in mobile app testing.

This paper is under CC 4.0 license.

Although we only experiment with Android mobile apps, since other platforms have these similar types of information, InputBlaster can be used to conduct the testing of input widgets for other platforms. We conduct a small-scale experiment for another two popular platforms, and experiment on 10 iOS apps with 15 bugs and 10 Web apps with 18 bugs, with details on our website. Results show that InputBlaster’s bug detection rate is 80% for iOS apps and 78% for Web apps within 30 minutes testing time.

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