Applicability of the Graph-RAG + LLM SRS Compliance Framework to Other Domains

Determine whether the automated framework that leverages Graph-RAG for retrieval and Large Language Models with Chain-of-Thought and Tree-of-Thought prompting to assess compliance of Software Requirements Specifications is applicable to domains beyond finance and aerospace that have different compliance requirements.

Background

The paper proposes an automated framework that builds a graph-based index from regulatory and higher-level requirement documents (Graph-RAG) and uses advanced LLM prompting strategies (Chain of Thought and Tree of Thought) to evaluate whether SRS requirements conform to those references. The framework is experimentally validated in two regulated environments: finance (an online broker application governed by national stock market standards) and aerospace (NASA’s X-38 Fault Tolerant System Services tied to the FTPP requirements).

While results show improved retrieval and reasoning performance compared to baseline RAG methods, the authors explicitly note that the study is domain-specific. They highlight that, because the evaluation is limited to finance and aerospace, it remains uncertain whether the same approach will generalize to other domains with differing compliance regimes, motivating further testing across diverse contexts to assess generalizability.

References

Since the framework was tested in regulated environments like finance and aerospace, its applicability to other domains with different compliance requirements remains uncertain.

— Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks  (2412.08593 - Masoudifard et al., 2024) in Section 6.2 Threats to Validity (External Validity)

Validating these expectations across domains remains future work, and we scope our claim accordingly: we contribute a transferable method together with one industrial demonstration of it, not evidence of cross-domain performance.

— Requirements-Augmented Generation for Trustworthy Acceptance Testing of LLM-Based Software  (2608.12970 - Wang et al., 13 Aug 2026) in Discussion, subsection “What porting the framework requires”