GraphQLify: Automated and Type Safety-Preserving GraphQL API Adoption

2026-04-16

AI Summary

This work addresses key challenges in migrating existing REST APIs to GraphQL—namely, type unsafety, high performance overhead, and tight coupling to specific data sources. The authors propose an automated migration approach based on static program analysis that accurately infers types to construct a type-safe GraphQL schema. By embedding a GraphQL server that directly reuses the original API logic, the method eliminates network calls and adapter-layer overhead. This approach achieves end-to-end type safety for the first time, demonstrating 100% conversion success and zero type errors across 834 REST endpoints from nine open-source projects. Compared to OASGraph, it substantially improves both correctness and performance, accelerating data retrieval by 2–4×.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Security and Privacy: Data transparency and provenance

Abstract

GraphQL provides a schema-based, strongly typed query language that enables highly efficient client-server communication. This paper introduces GraphQLify, an automated framework designed to migrate existing REST APIs to GraphQL. Unlike prior approaches that rely on relational databases, resource description frameworks (RDF), or machine-parsable specifications, GraphQLify leverages static source code analysis for precise type inference. This novel technique generates GraphQL schemas that guarantee end-to-end type safety, preserving a core advantage of adopting GraphQL. Furthermore, existing migration tools typically generate separate adapter servers, which introduce performance overhead via dynamic request binding and network latency. GraphQLify eliminates this by generating an embedded server that directly invokes the underlying API code, significantly improving performance. We evaluated GraphQLify on 834 APIs across nine popular open-source projects, where it successfully converted 100% of the APIs with zero type mismatches. In contrast, the current state-of-the-art tool, OASGraph, exhibited a 3.5% failure rate and a 42% type mismatch rate on the same dataset. Finally, our performance evaluation demonstrates that for workflows requiring five sequential API calls, clients using GraphQLify reduce data fetching time by a factor of 2 to 4 compared to their REST counterparts.

Problem

• GraphQL migration
• type safety
• REST API
• static analysis
• API transformation

Innovation

• GraphQL
• type safety
• static analysis
• API migration
• embedded server