A Multi-Agent RAG Framework for Regulatory Compliance Checking of Software Requirements
Bibliographic record
Abstract
Ensuring compliance with regulations poses considerable challenges for software development, particularly during the requirements specification phase. Traditional methods rely heavily on manual inspections that are time-consuming and prone to errors. This research proposes an innovative framework that leverages the synergy of multiple AI agents to automate software requirement compliance verification partially. The framework integrates Large Language Models (LLMs), prompt engineering, and Retrieval-Augmented Generation (RAG) to analyze, detect, and revise non-compliant requirements. The core of our proposal lies in multi-agent communication, where distinct AI agents collaborate to achieve the overarching goal of compliance checking. LLMs comprehend requirements specifications, while prompt engineering guides LLMs toward compliance-related aspects. The RAG techniques detect non-compliant requirements and suggest changes. Finally, a robust Human-in-the-Loop mechanism ensures accuracy, reliability, and adaptability. A tool, available online, is implemented to translate the technology for effective application. We discuss its ability to identify non-compliant requirements in an extensive experimental evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".