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Technical University of Munich

Using ChatGPT to Replace Human in App Testing to Ensure App Usability and Accessibility

Bachelor & Master Thesis

Mobile apps now have become the most popular way of accessing the Internet as well as performing daily. Different from traditional desktop applications, mobile apps are typically developed under the time-to-market pressure and facing fierce competition — over 3.8 million Android apps and 2 million iPhone apps are striving to gain users on Google Play and Apple App Store, the two primary mobile app markets. Therefore, for app developers and companies, it is crucial to accelerate the mobile app development process.

Product teams always need to conduct a user study with real and targeted users once the product is developed to test the usability and potential bugs in the products; however, this process is always time-consuming and costly. The team may need to find people of different backgrounds, train them, and then spend time with the users when they are doing the study. Moreover, they always need to conduct several rounds of usability tests every time they iterate the product based on the feedback from the previous study or because of the new requirements from product managers. The emergence of large language models (LLMs) these years reveals a chance to have them replace/assist humans in many different areas [1, 2]. In this topic, we especially focus on ChatGPT, which is a conversational-based LLM that thrives in different fields such as news press, business, programming, and education. We want to explore if ChatGPT has the capability to imitate different end-users (e.g., older adults, children, blind users) to test the usability of products.

Required knowledge:

  • Mobile app development
  • Strong programming background.
  • Basic knowledge about AI/ML and Human-Computer Interaction is a plus.

Note that this project can be carried out remotely. Students with great performance may be granted an opportunity to do a paid Hiwi in the coming semester break and even a PhD position in the future.

Reference:

[1] Liu Z, Chen C, Wang J, Che X, Huang Y, Hu J, Wang Q. Fill in the blank: Context-aware automated text input generation for mobile gui testing. In2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) 2023 May 14 (pp. 1355-1367). IEEE.
[2] Feng S, Chen C. Prompting Is All Your Need: Automated Android Bug Replay with Large Language Models. In 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE)

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