Towards Efficient Record and Replay: A Case Study in WeChat

Feng, Sidong; Lu, Haochuan; Xiong, Ting; Deng, Yuetang; Chen, Chunyang

doi:10.1145/3611643.3613880

Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Enginee 2023

DOI: 10.1145/3611643.3613880

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Towards Efficient Record and Replay: A Case Study in WeChat

Sidong Feng,

Haochuan Lu,

Ting Xiong

et al.

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2024

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Cited by 3 publications

References 38 publications

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MUD: Towards a Large-Scale and Noise-Filtered UI Dataset for Modern Style UI Modeling

Feng,

Ma,

Wang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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The importance of computational modeling of mobile user interfaces (UIs) is undeniable. However, these require a high-quality UI dataset. Existing datasets are often outdated, collected years ago, and are frequently noisy with mismatches in their visual representation. This presents challenges in modeling UI understanding in the wild. This paper introduces a novel approach to automatically mine UI data from Android apps, leveraging Large Language Models (LLMs) to mimic human-like exploration. To ensure dataset quality, we employ the best practices in UI noise filtering and incorporate human annotation as a final validation step. Our results demonstrate the effectiveness of LLMs-enhanced app exploration in mining more meaningful UIs, resulting in a large dataset MUD of 18k human-annotated UIs from 3.3k apps. We highlight the usefulness of MUD in two common UI modeling tasks: element detection and UI retrieval, showcasing its potential to establish a foundation for future research into high-quality, modern UIs. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI).

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MUD: Towards a Large-Scale and Noise-Filtered UI Dataset for Modern Style UI Modeling

Feng,

Ma,

Wang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

show abstract

Enabling Cost-Effective UI Automation Testing with Retrieval-Based LLMs: A Case Study in WeChat

Feng,

Lu,

Jiang

et al. 2024

Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering

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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 industriallevel 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 realworld WeChat testing platform, demonstrating its usefulness in detecting 141 bugs and enhancing the developers' testing process. CCS CONCEPTS• Software and its engineering → Software testing and debugging.

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Towards a Robust Waiting Strategy for Web GUI Testing for an Industrial Software System

Zhang,

Liao,

Ding

et al. 2024

Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Towards Efficient Record and Replay: A Case Study in WeChat

Cited by 3 publications

References 38 publications

MUD: Towards a Large-Scale and Noise-Filtered UI Dataset for Modern Style UI Modeling

MUD: Towards a Large-Scale and Noise-Filtered UI Dataset for Modern Style UI Modeling

Enabling Cost-Effective UI Automation Testing with Retrieval-Based LLMs: A Case Study in WeChat

Towards a Robust Waiting Strategy for Web GUI Testing for an Industrial Software System

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