FeedAIde: Guiding App Users to Submit Rich Feedback Reports by Asking Context-Aware Follow-Up Questions
Ali Ebrahimi Pourasad, Meyssam Saghiri, Walid Maalej

TL;DR
FeedAIde is a context-aware, AI-powered feedback tool that guides app users to submit detailed reports by asking adaptive follow-up questions, improving report quality and developer insights.
Contribution
We introduce FeedAIde, a novel framework leveraging multimodal large language models to enhance user feedback quality through context-aware, interactive questioning.
Findings
Participants found FeedAIde easier and more helpful than simple forms.
FeedAIde increased the completeness of bug reports and feature requests.
Expert assessment confirmed improved report quality.
Abstract
User feedback is essential for the success of mobile apps, yet what users report and what developers need often diverge. Research shows that users often submit vague feedback and omit essential contextual details. This leads to incomplete reports and time-consuming clarification discussions. To overcome this challenge, we propose FeedAIde, a context-aware, interactive feedback approach that supports users during the reporting process by leveraging the reasoning capabilities of Multimodal Large Language Models. FeedAIde captures contextual information, such as the screenshot where the issue emerges, and uses it for adaptive follow-up questions to collaboratively refine with the user a rich feedback report that contains information relevant to developers. We implemented an iOS framework of FeedAIde and evaluated it on a gym's app with its users. Compared to the app's simple feedback form,…
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Taxonomy
TopicsSoftware Engineering Techniques and Practices · Software Engineering Research · Software Testing and Debugging Techniques
