Leveraging LLMs for Generation of Unusual Text Inputs in Mobile App Tests: Experiment Design

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Leveraging LLMs for Generation of Unusual Text Inputs in Mobile App Tests: Experiment Design
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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.

and Sloth . They aim at generating the strings that violate the constraints , which is similar to our task. ’s key idea is to generate the test strings based on heuristic rules. Sloth proposes to exploit succinct alternating finite-state automata as concise symbolic representations of string constraints. There are constraint-based methods, i.e., Mobolic and TextExerciser , which can generate diversified inputs for testing the app. For example, TextExerciser utilizes the dynamic hints to guide it in producing the inputs.

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