LELANTE: LEveraging LLM for Automated ANdroid TEsting

• URL: http://arxiv.org/abs/2504.20896v1
• Date: Tue, 29 Apr 2025 16:13:49 GMT
• Title: LELANTE: LEveraging LLM for Automated ANdroid TEsting
• Authors: Shamit Fatin, Mehbubul Hasan Al-Quvi, Haz Sameen Shahgir, Sukarna Barua, Anindya Iqbal, Sadia Sharmin, Md. Mostofa Akbar, Kallol Kumar Pal, A. Asif Al Rashid,
• Abstract summary: Existing testing approaches require developers to manually write scripts using tools such as Appium and Espresso to execute the corresponding test case.<n>We introduce LELANTE, a novel framework that utilizes large language models (LLMs) to automate test case execution without requiring pre-written scripts.<n>In experiments across 390 test cases spanning 10 popular Android applications, LELANTE achieved a 73% test execution success rate.
• Score: 6.112769800569302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given natural language test case description for an Android application, existing testing approaches require developers to manually write scripts using tools such as Appium and Espresso to execute the corresponding test case. This process is labor-intensive and demands significant effort to maintain as UI interfaces evolve throughout development. In this work, we introduce LELANTE, a novel framework that utilizes large language models (LLMs) to automate test case execution without requiring pre-written scripts. LELANTE interprets natural language test case descriptions, iteratively generate action plans, and perform the actions directly on the Android screen using its GUI. LELANTE employs a screen refinement process to enhance LLM interpretability, constructs a structured prompt for LLMs, and implements an action generation mechanism based on chain-of-thought reasoning of LLMs. To further reduce computational cost and enhance scalability, LELANTE utilizes model distillation using a foundational LLM. In experiments across 390 test cases spanning 10 popular Android applications, LELANTE achieved a 73% test execution success rate. Our results demonstrate that LLMs can effectively bridge the gap between natural language test case description and automated execution, making mobile testing more scalable and adaptable.

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