Automated Test Transfer Across Android Apps Using Large Language Models

TL;DR

This paper presents LLMigrate, a novel approach using Large Language Models to transfer UI tests across Android apps, significantly improving success rates and reducing manual effort compared to previous methods.

Contribution

Introduces LLMigrate, the first LLM-based technique for cross-app UI test transfer that handles real-world variations effectively.

Findings

97.5% success rate in test transfer

91.1% reduction in manual testing effort

38.2% improvement over prior methods

Abstract

The pervasiveness of mobile apps in everyday life necessitates robust testing strategies to ensure quality and efficiency, especially through end-to-end usage-based tests for mobile apps' user interfaces (UIs). However, manually creating and maintaining such tests can be costly for developers. Since many apps share similar functionalities beneath diverse UIs, previous works have shown the possibility of transferring UI tests across different apps within the same domain, thereby eliminating the need for writing the tests manually. However, these methods have struggled to accommodate real-world variations, often facing limitations in scenarios where source and target apps are not very similar or fail to accurately transfer test oracles. This paper introduces an innovative technique, LLMigrate, which leverages Large Language Models (LLMs) to efficiently transfer usage-based UI tests across mobile apps. Our experimental evaluation shows LLMigrate can achieve a 97.5% success rate in automated test transfer, reducing the manual effort required to write tests from scratch by 91.1%. This represents an improvement of 9.1% in success rate and 38.2% in effort reduction compared to the best-performing prior technique, setting a new benchmark for automated test transfer.