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cloud.google.com·7h ago
AI Research
arXiv CS.AI·27d ago·5 min read
Natural-Language to SysMLv2 Translation via Conformance-Driven Iterative Refinement
TL;DR
A framework for translating natural language into SysMLv2 models was developed, achieving 100% production conformance through a conformance-checker-driven iterative refinement process.
✦ Why It Matters
Implement this framework to ensure your natural language requirements translate accurately into deployable SysMLv2 models.
Key Takeaways
- Utilize the conformance-checker-driven framework for reliable model generation.
- Achieve 100% production conformance with iterative refinement.
- Test your models against the SysMLv2 conformance checker.
- Transform natural language requirements into executable SysMLv2 artifacts.
How It Works
The framework operates by embedding a SysMLv2 conformance checker within a generate-check-repair loop. Each generated model is evaluated for conformance, and any errors are diagnosed and corrected iteratively until the model meets production standards.