RITA: A Tool for Automated Requirements Classification and Specification from Online User Feedback

2026-01-16

AI Summary

This work addresses the challenge of processing large-scale, noisy online user feedback in industrial settings, where existing requirements engineering tools lack end-to-end integration and practical applicability. To bridge the gap between academic research and industrial practice, we propose and implement a unified workflow based on a lightweight open-source large language model. This approach integrates, for the first time, feedback classification, non-functional requirement identification, and natural language requirements specification generation into a cohesive, end-to-end toolchain. The system features an intuitive user interface and seamless integration with Jira, significantly enhancing the automation and efficiency of transforming raw user feedback into structured, actionable requirements. Our solution demonstrates a marked improvement in scalability and usability, effectively supporting real-world requirements engineering workflows.

Technology Category

• Natural Language Processing: (Large) Language Models
• Humans and AI: User Experience and Usability
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

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.

Problem

• requirements engineering
• online user feedback
• end-to-end integration
• tool adoption
• requirements classification

Innovation

• requirements engineering
• large language models
• user feedback analysis
• automated classification
• Jira integration