ATLAS: A Layered Constraint-Guided Framework for Structured Artifact Generation in LLM-Assisted MDE

TL;DR

ATLAS is a structured, constraint-guided framework that improves the generation of engineering artifacts by integrating domain models, constraints, and validation within a model-driven workflow, demonstrated on AUTOSAR.

Contribution

It introduces a layered, constraint-guided generation approach that ensures schema validity and defect diagnosability in large language model-assisted artifact creation.

Findings

ATLAS produces schema-valid AUTOSAR artifacts at both single and multi-file scales.

It maintains perfect file completeness and XSD validity in multi-file generation.

Residual system defects are exposed through validation checks, enabling diagnosability.

Abstract

ATLAS is a constraint-guided generation framework for structured engineering artifacts whose outputs must satisfy explicit schemas, domain rules, and audit requirements. Rather than treating a large language model as a standalone generator, ATLAS places generation inside a model-driven workflow that separates domain representation, constraint compilation, and post-generation validation. ATLAS combines three components. A metamodel-integration stage builds a typed representation of domain entities and relations; in this study, it operates over authoritative AUTOSAR meta-model assets. An Integrated Constraint Model (ICM) compiles heterogeneous requirements into two operational layers: generation-time structural constraints and post-generation semantic/logical obligations. Constraint-Guided, Validation-Backed Generation (CVG) then combines Layer~1 constrained decoding, Layer~2 backend validation, and audit-guided repair. In the AUTOSAR instantiation, these Layer~2 obligations are realized through SHACL/SMT-style checks, illustrating how the same ICM can be connected to domain-specific validation backends. We evaluate ATLAS on AUTOSAR artifact generation at both single-file and multi-file scales. In the evaluated AUTOSAR setting, ATLAS consistently produces schema-valid single-file outputs and preserves perfect file completeness and XSD validity at multi-file scale, while SHACL/SMT checks and result analysis continue to expose residual system-level defects. The empirical picture is therefore one of bounded automation: ATLAS secures structural validity and turns higher-level failures into explicit, diagnosable objects within the generation workflow.