Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks (2412.08593v1)

Abstract: Ensuring that Software Requirements Specifications (SRS) align with higher-level organizational or national requirements is vital, particularly in regulated environments such as finance and aerospace. In these domains, maintaining consistency, adhering to regulatory frameworks, minimizing errors, and meeting critical expectations are essential for the reliable functioning of systems. The widespread adoption of LLMs highlights their immense potential, yet there remains considerable scope for improvement in retrieving relevant information and enhancing reasoning capabilities. This study demonstrates that integrating a robust Graph-RAG framework with advanced prompt engineering techniques, such as Chain of Thought and Tree of Thought, can significantly enhance performance. Compared to baseline RAG methods and simple prompting strategies, this approach delivers more accurate and context-aware results. While this method demonstrates significant improvements in performance, it comes with challenges. It is both costly and more complex to implement across diverse contexts, requiring careful adaptation to specific scenarios. Additionally, its effectiveness heavily relies on having complete and accurate input data, which may not always be readily available, posing further limitations to its scalability and practicality.

• The paper demonstrates that combining Graph-RAG with sophisticated prompting techniques significantly enhances automated requirement traceability and compliance checks.
• It employs graph-based retrieval alongside IO, CoT, and ToT strategies to build knowledge graphs that validate Software Requirements Specifications against regulatory standards.
• Evaluation with models like GPT-4o reveals improved precision in violation detection, though increased computational complexity highlights practical limitations.

Enhancing Automated Requirement Traceability Through Graph-RAG and Advanced Prompting

The paper in question presents an investigation into improving automated compliance checks and requirement traceability by integrating Graph Retrieval-Augmented Generation (Graph-RAG) systems with advanced prompt engineering techniques. This research addresses the complexities associated with validating Software Requirements Specifications (SRS) against stringent organizational and regulatory frameworks, which is of particular importance in regulated domains such as finance and aerospace.

Overview of Methods and Objectives

The paper's primary contributions are multi-faceted, involving both the integration of innovative retrieval-augmented techniques and the exploration of sophisticated prompting methods to enhance reasoning capabilities of LLMs. At its core, the research employs Graph-RAG to build knowledge graphs capturing relationships between regulatory articles, standards, and software requirements. This graph-based retrieval enhances traditional RAG by not only identifying text with semantic relevance but also considering relationships between entities, thus allowing for contextually rich analysis.

Prompt engineering techniques play a crucial role in this paper. Three prompting strategies are explored: Input/Output (IO), Chain of Thought (CoT), and Tree of Thought (ToT). While IO represents a baseline for comparison, CoT and ToT are more advanced methodologies designed to facilitate step-by-step reasoning and multi-path exploration, respectively. These strategies are used to verify whether an SRS adheres to referenced regulations, enabling more precise compliance checks.

Evaluation and Results

The efficacy of Graph-RAG combined with various LLMs, specifically GPT-4o and GPT-4o-mini, is evaluated across multiple performance metrics. The results reveal that Graph-RAG, especially when paired with GPT-4o, significantly outperforms the baseline RAG method. The nuanced and complex nature of traceability tasks benefits markedly from graph-based retrieval, as the constructed graphs yield a more comprehensive understanding of the contextual dependencies and regulatory alignments.

Among the prompting methods, ToT generally provides the most robust and precise reasoning outcomes, resulting in higher precision in violation detection. This superior performance underscores the importance of incorporating complex reasoning pathways into compliance checking tasks, enhancing both the model's interpretability and accuracy.

Implications and Future Research

This research contributes to the domain by offering a methodological advancement in the use of LLMs for automated compliance and traceability tasks. The integration of graph-centric retrieval mechanisms with sophisticated prompting techniques presents a rigorous approach for aligning software requirements with regulatory demands. This alignment is crucial in reducing error propagation and ensuring adherence to standards at the early stages of software development.

However, the paper also notes the increased computational complexity and costs associated with these advanced methods, particularly when employing resource-intensive models like GPT-4o. This highlights a practical limitation that future research might address, potentially through optimizations or exploring alternative and cost-effective LLM architectures.

Looking ahead, further research could expand the application of these techniques beyond the domains studied here, addressing diverse regulatory landscapes and varied SRS formats. Additionally, exploring real-time updates to knowledge graphs and integrating dynamic change detection could further enhance the utility and scalability of the Graph-RAG approach in large-scale, evolving regulatory environments.

In summary, this paper provides valuable insights into leveraging graph-based retrieval and advanced prompting techniques for requirement traceability, paving the way for future advances in AI-driven compliance solutions.