用AI智能追问，让用户提交更完整的反馈报告。
FeedAIde: Guiding App Users to Submit Rich Feedback Reports by Asking Context-Aware Follow-Up Questions
- 基于多模态大模型，根据截图等上下文动态提问。
- 用户反馈完整度提升，专家评估显示质量显著提高。
- 适合希望改进用户反馈质量的移动应用团队。
用户反馈对移动应用成功至关重要，但用户提交的内容常模糊且缺少关键上下文，导致报告不完整，开发者需反复澄清。为此，我们提出FeedAIde，一种基于上下文感知的交互式反馈方法，利用多模态大语言模型的推理能力，在用户报告过程中自动捕获截图等上下文信息，并生成自适应的追问问题，与用户协作完善反馈内容。我们在一款健身类App上实现了iOS版本的FeedAIde，并通过真实用户测试验证。相比原简单表单，用户认为FeedAIde更易用、更有帮助。两名行业专家评估了54份反馈报告，结果显示其在缺陷报告和功能建议的完整性方面均有显著提升。研究证明，上下文感知的生成式AI反馈系统能有效改善用户体验并提升开发者的信息获取效率。
原文摘要 · Abstract (English)
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, participants rated FeedAIde as easier and more helpful for reporting feedback. An assessment by two industry experts of the resulting 54 reports showed that FeedAIde improved the quality of both bug reports and feature requests, particularly in terms of completeness. The findings of our study demonstrate the potential of context-aware, GenAI-powered feedback reporting to enhance the experience for users and increase the information value for developers.
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