GraphQLify automates REST-to-GraphQL migration via static source code analysis, delivering 100% type-safe conversions on 834 APIs and 2-4x faster performance than REST for multi-call workflows.
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Forge pipeline combines LLM code generation with MDE transformations to produce verifiable artifacts in Dafny, CSP, and Isabelle, iterating on failures to generate standards-relevant evidence for Java code.
CoCoMUT is a reusable pipeline that discovers project structure, constructs call graphs, extracts source, reconciles bytecode to source, and emits versioned JSON datasets of method contexts, demonstrated on 20 Java repositories with 97.8% reconciliation and 99% audit accuracy.
citing papers explorer
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GraphQLify: Automated and Type Safety-Preserving GraphQL API Adoption
GraphQLify automates REST-to-GraphQL migration via static source code analysis, delivering 100% type-safe conversions on 834 APIs and 2-4x faster performance than REST for multi-call workflows.
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Formal-Method-Guided Vibe Coding: Closing the Verification Loop on AI-Generated Safety-Critical Software Through Model-Driven Engineering
Forge pipeline combines LLM code generation with MDE transformations to produce verifiable artifacts in Dafny, CSP, and Isabelle, iterating on failures to generate standards-relevant evidence for Java code.
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CoCoMUT: A Tool for Code-Context Mining and Automated Dataset Generation
CoCoMUT is a reusable pipeline that discovers project structure, constructs call graphs, extracts source, reconciles bytecode to source, and emits versioned JSON datasets of method contexts, demonstrated on 20 Java repositories with 97.8% reconciliation and 99% audit accuracy.