Projects per year
Abstract
UI automation tests play a crucial role in ensuring the quality of mobile applications. Despite the growing popularity of machine learning techniques to generate these tests, they still face several challenges, such as the mismatch of UI elements. The recent advances in Large Language Models (LLMs) have addressed these issues by leveraging their semantic understanding capabilities. However, a significant gap remains in applying these models to industrial-level app testing, particularly in terms of cost optimization and knowledge limitation. To address this, we introduce CAT to create cost-effective UI automation tests for industry apps by combining machine learning and LLMs with best practices. Given the task description, CAT employs Retrieval Augmented Generation (RAG) to source examples of industrial app usage as the few-shot learning context, assisting LLMs in generating the specific sequence of actions. CAT then employs machine learning techniques, with LLMs serving as a complementary optimizer, to map the target element on the UI screen. Our evaluations on the WeChat testing dataset demonstrate the CAT's performance and cost-effectiveness, achieving 90% UI automation with $0.34 cost, outperforming the state-of-the-art. We have also integrated our approach into the real-world WeChat testing platform, demonstrating its usefulness in detecting 141 bugs and enhancing the developers' testing process.
Original language | English |
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Title of host publication | Proceedings - 2024 39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024 |
Editors | Lin Shi |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 1973-1978 |
Number of pages | 6 |
ISBN (Electronic) | 9798400712487 |
DOIs | |
Publication status | Published - 2024 |
Event | Automated Software Engineering Conference 2024 - Sacramento, United States of America Duration: 28 Oct 2024 → 1 Nov 2024 Conference number: 39th https://dl.acm.org/doi/proceedings/10.1145/3691620 (Proceedings) https://conf.researchr.org/home/ase-2024 (Website) |
Conference
Conference | Automated Software Engineering Conference 2024 |
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Abbreviated title | ASE 2024 |
Country/Territory | United States of America |
City | Sacramento |
Period | 28/10/24 → 1/11/24 |
Other | ACM/IEEE International Conference on Automated Software Engineering, ASE 2024 |
Internet address |
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Keywords
- cost optimization
- large language model
- retrieval-augmented generation
- UI automation test
Projects
- 1 Active
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Mapping the Effectiveness of Automated Software Testing
Aleti, A. (Primary Chief Investigator (PCI)) & Turhan, B. (Chief Investigator (CI))
13/09/21 → 12/09/25
Project: Research