Multilingual Crowd-Based Requirements Engineering Using Large Language Models

TL;DR

This paper introduces DeeperMatcher, an LLM-powered tool for crowd-based requirements engineering that helps match user feedback with development issues across multiple languages, validated on English and Portuguese datasets.

Contribution

It presents a novel LLM-based approach for multilingual crowd-based requirements engineering, including a prototype tool and initial validation on diverse language datasets.

Findings

Accuracy depends on text embedding method used

Effective for English and Portuguese datasets

Highlights need for further refinement for reliability

Abstract

A central challenge for ensuring the success of software projects is to assure the convergence of developers' and users' views. While the availability of large amounts of user data from social media, app store reviews, and support channels bears many benefits, it still remains unclear how software development teams can effectively use this data. We present an LLM-powered approach called DeeperMatcher that helps agile teams use crowd-based requirements engineering (CrowdRE) in their issue and task management. We are currently implementing a command-line tool that enables developers to match issues with relevant user reviews. We validated our approach on an existing English dataset from a well-known open-source project. Additionally, to check how well DeeperMatcher works for other languages, we conducted a single-case mechanism experiment alongside developers of a local project that has issues and user feedback in Brazilian Portuguese. Our preliminary analysis indicates that the accuracy of our approach is highly dependent on the text embedding method used. We discuss further refinements needed for reliable crowd-based requirements engineering with multilingual support.