Automating the Detection of Requirement Dependencies Using Large Language Models

2026-02-25

AI Summary

This study addresses the inefficiency and error-proneness of manual identification of dependencies among natural language requirements in complex software systems, particularly in the face of high coupling, ambiguity, and frequent changes. To overcome these challenges, we propose LEREDD, a novel approach that integrates Retrieval-Augmented Generation (RAG) with In-Context Learning (ICL) to leverage large language models for automatically detecting multiple fine-grained dependency types. We construct a labeled dataset comprising 813 requirement pairs and evaluate our method experimentally, achieving an accuracy of 0.93 and an F1 score of 0.84. Notably, the non-dependency class attains an average F1 score of 0.96, and the “Requires” dependency type shows an improvement of over 94% in average F1 compared to baseline methods, significantly enhancing both precision and generalization in requirement dependency detection.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Requirements are inherently interconnected through various types of dependencies. Identifying these dependencies is essential, as they underpin critical decisions and influence a range of activities throughout software development. However, this task is challenging, particularly in modern software systems, given the high volume of complex, coupled requirements. These challenges are further exacerbated by the ambiguity of Natural Language (NL) requirements and their constant change. Consequently, requirement dependency detection is often overlooked or performed manually. Large Language Models (LLMs) exhibit strong capabilities in NL processing, presenting a promising avenue for requirement-related tasks. While they have shown to enhance various requirements engineering tasks, their effectiveness in identifying requirement dependencies remains unexplored. In this paper, we introduce LEREDD, an LLM-based approach for automated detection of requirement dependencies that leverages Retrieval-Augmented Generation (RAG) and In-Context Learning (ICL). It is designed to identify diverse dependency types directly from NL requirements. We empirically evaluate LEREDD against two state-of-the-art baselines. The results show that LEREDD provides highly accurate classification of dependent and non-dependent requirements, achieving an accuracy of 0.93, and an F1 score of 0.84, with the latter averaging 0.96 for non-dependent cases. LEREDD outperforms zero-shot LLMs and baselines, particularly in detecting fine-grained dependency types, where it yields average relative gains of 94.87% and 105.41% in F1 scores for the Requires dependency over the baselines. We also provide an annotated dataset of requirement dependencies encompassing 813 requirement pairs across three distinct systems to support reproducibility and future research.

Problem

• requirement dependencies
• natural language requirements
• software requirements
• dependency detection
• requirements engineering

Innovation

• Large Language Models
• Requirement Dependencies
• Retrieval-Augmented Generation
• In-Context Learning
• Automated Requirements Engineering