Automated Test Transfer Across Android Apps Using Large Language Models
Benyamin Beyzaei, Saghar Talebipour, Ghazal Rafiei, Nenad Medvidovic, and Sam Malek

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
