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.

翻译：用户反馈对于移动应用的成功至关重要，但用户报告的内容与开发者所需的信息往往存在偏差。研究表明，用户常常提交模糊的反馈，并遗漏关键的上下文细节。这导致报告不完整，并引发耗时的澄清讨论。为应对这一挑战，我们提出了FeedAIde，一种上下文感知的交互式反馈方法，该方法利用多模态大语言模型的推理能力，在用户报告过程中为其提供支持。FeedAIde捕获上下文信息（例如问题出现的屏幕截图），并利用这些信息生成自适应的后续问题，与用户协作完善一份包含对开发者有用信息的丰富反馈报告。我们实现了FeedAIde的iOS框架，并在一款健身应用及其用户中进行了评估。与应用原有的简单反馈表单相比，参与者认为FeedAIde在报告反馈时更易于使用且更有帮助。两位行业专家对由此产生的54份报告进行的评估表明，FeedAIde提高了错误报告和功能请求的质量，尤其是在完整性方面。我们的研究结果表明，基于上下文感知和生成式人工智能的反馈报告具有提升用户体验并为开发者增加信息价值的潜力。