Context and motivation. Online user feedback is a valuable resource for requirements engineering, but its volume and noise make analysis difficult. Existing tools support individual feedback analysis tasks, but their capabilities are rarely integrated into end-to-end support. Problem. The lack of end-to-end integration limits the practical adoption of existing RE tools and makes it difficult to assess their real-world usefulness. Solution. To address this challenge, we present RITA, a tool that integrates lightweight open-source large language models into a unified workflow for feedback-driven RE. RITA supports automated request classification, non-functional requirement identification, and natural-language requirements specification generation from online feedback via a user-friendly interface, and integrates with Jira for seamless transfer of requirements specifications to development tools. Results and conclusions. RITA exploits previously evaluated LLM-based RE techniques to efficiently transform raw user feedback into requirements artefacts, helping bridge the gap between research and practice. A demonstration is available at: https://youtu.be/8meCLpwQWV8.

翻译：背景与动机。在线用户反馈是需求工程中的宝贵资源，但其海量性与噪声使得分析工作变得困难。现有工具虽支持个别反馈分析任务，但其功能鲜少被整合为端到端的支持体系。问题。端到端整合的缺失限制了现有需求工程工具的实际应用，并难以评估其在实际场景中的有效性。解决方案。为应对这一挑战，我们提出了RITA，该工具将轻量级开源大语言模型集成到一个统一的反馈驱动需求工程工作流中。RITA通过用户友好界面，支持从在线反馈中自动化进行请求分类、非功能性需求识别以及自然语言需求规约生成，并与Jira集成以实现需求规约向开发工具的无缝传递。结果与结论。RITA利用先前已评估过的基于大语言模型的需求工程技术，有效地将原始用户反馈转化为需求制品，有助于弥合研究与实践之间的鸿沟。演示视频可访问：https://youtu.be/8meCLpwQWV8。