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

TL;DR

RITA is an integrated tool leveraging open-source large language models to automate classification, identification, and specification of requirements from noisy online user feedback, streamlining requirements engineering workflows.

Contribution

It introduces an end-to-end RE tool that combines multiple LLM-based techniques into a unified, user-friendly platform supporting real-world feedback analysis.

Findings

Efficient transformation of raw feedback into requirements artifacts.

Seamless integration with Jira enhances practical applicability.

Demonstrated effectiveness through a live demonstration.

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.