ReXCL: A Tool for Requirement Document Extraction and Classification

2025-04-10

AI Summary

To address the challenges of unstructured requirements documents, low efficiency, and error-proneness in manual processing, this paper proposes a dual-module framework for requirements engineering automation. The framework integrates heuristic text rules with an adaptively fine-tuned Transformer encoder to achieve end-to-end schematization of semi-structured requirements—jointly performing information extraction, standardized schema mapping, and automatic semantic labeling from raw textual input. It supports direct export to mainstream requirements management tools and seamless integration into existing RE toolchains. As the first systematic solution targeting structured transformation of requirements documentation, it achieves an average 18.7% improvement in F1-score across multiple industrial datasets, significantly enhancing classification accuracy and engineering reusability.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Knowledge Representation and Reasoning: Knowledge Engineering
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

This paper presents the ReXCL tool, which automates the extraction and classification processes in requirement engineering, enhancing the software development lifecycle. 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

• Automates extraction and classification in requirement engineering
• Processes raw documents into predefined schema using AI
• Improves efficiency and accuracy in managing software requirements

Innovation

• Automates extraction and classification in requirement engineering
• Uses heuristics and predictive modeling for processing
• Adaptive fine-tuning of encoder-based models for classification