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Generating Accurate OpenAPI Descriptions from Java Source Code

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arxiv 2410.23873 v1 pith:OZGZJEK4 submitted 2024-10-31 cs.SE

classification cs.SE
keywords restaccurateautooashigheropenapiapisapproachapproaches
verification ladder T0 review T1 audit T2 compute T3 formal

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Developers require accurate descriptions of REpresentational State Transfer (REST) Application Programming Interfaces (APIs) for a successful interaction between web services. The OpenAPI Specification (OAS) has become the de facto standard for documenting REST APIs. Manually creating an OpenAPI description is time-consuming and error-prone, and therefore several approaches were proposed to automatically generate them from bytecode or runtime information. In this paper, we first study three state-of-the-art approaches, Respector, Prophet, and springdoc-openapi, and present and discuss their shortcomings. Next, we introduce AutoOAS, our approach addressing these shortcomings to generate accurate OpenAPI descriptions. It detects exposed REST endpoint paths, corresponding HTTP methods, HTTP response codes, and the data models of request parameters and responses directly from Java source code. We evaluated AutoOAS on seven real-world Spring Boot projects and compared its performance with the three state-of-the-art approaches. Based on a manually created ground truth, AutoOAS achieved the highest precision and recall when identifying REST endpoint paths, HTTP methods, parameters, and responses. It outperformed the second-best approach, Respector, with a 39% higher precision and 35% higher recall when identifying parameters and a 29% higher precision and 11% higher recall when identifying responses. Furthermore, AutoOAS is the only approach that handles configuration profiles, and it provided the most accurate and detailed description of the data models that were used in the REST APIs.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. OOPS: Automated generation of REST API specification via LLMs

    cs.SE 2026-01 conditional novelty 6.0 of 10

    OOPS uses LLM agents and an API dependency graph to generate OpenAPI specs from REST API server code across multiple languages and frameworks, with reported F1 above 92% on 12 APIs.

  2. LRASGen: LLM-based RESTful API Specification Generation

    cs.SE 2025-04 reject novelty 5.0 of 10

    LRASGen prompts GPT-4o mini or DeepSeek V3 to extract endpoints, parameters, constraints, and responses from RESTful API source code and assemble OpenAPI specs, reporting near-perfect accuracy on 20 APIs.

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