Read, Extract, Classify: A Tool for Smarter Requirements Engineering

2026-05-11

AI Summary

This work addresses the inefficiency and insufficient accuracy inherent in extracting and classifying requirements from semi-structured documents within traditional requirements engineering. To overcome these limitations, the authors propose ReXCL, an end-to-end automated tool that integrates heuristic rules with predictive modeling for requirement extraction and employs an encoder-based deep learning architecture with adaptive fine-tuning to achieve high-precision classification. The output of ReXCL is designed for seamless integration into mainstream requirements engineering tools. Empirical evaluation in real-world requirements engineering scenarios demonstrates that the proposed approach significantly enhances both processing efficiency and classification accuracy, confirming its effectiveness and practical utility.

Abstract

This paper presents the ReXCL tool, which automates the extraction and classification processes in requirements engineering, enhancing the software development life-cycle. The tool features two main modules: Extraction, which processes raw requirement documents into a predefined schema using heuristics and predictive modeling, and Classification, which assigns class labels to requirements using adaptive fine-tuning of encoder-based models. The final output can be exported to external requirement engineering tools. Performance evaluations indicate that ReXCL significantly improves efficiency and accuracy in managing requirements, marking a novel approach to automating the schematization of semi-structured requirement documents.

Problem

• requirements engineering
• requirement extraction
• requirement classification
• semi-structured documents
• software development

Innovation

• requirements engineering
• automated extraction
• adaptive fine-tuning
• encoder-based models
• schema generation