RANCANG BANGUN PLUGIN VS CODE EXTENSION UNTUK DOKUMENTASI OTOMATIS REST API DENGAN STANDAR OPENAPI BERBASIS TOOLS AI

Mohamad Aenur Rokhman, NIM.: 21106050081 (2026) RANCANG BANGUN PLUGIN VS CODE EXTENSION UNTUK DOKUMENTASI OTOMATIS REST API DENGAN STANDAR OPENAPI BERBASIS TOOLS AI. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

REST API documentation is a critical component in modern software development based on microservices architecture; however, its creation still relies heavily on manual annotation, which is prone to documentation drift and documentation smells. This study aims to design and implement a Visual Studio Code Extension plugin capable of automatically generating REST API documentation in compliance with the OpenAPI 3.1 standard by leveraging the capabilities of AI-based Large Language Models (LLMs). The developed system integrates the Gemma 3 12B-IT model through the OpenRouter AI inference platform, employing a few-shot learning-based prompt engineering mechanism to produce syntactically valid YAML or JSON output. The development methodology adopts an Agile approach with an iterative and adaptive Kanban framework. The system architecture follows a client-server pattern in which the VS Code plugin acts as a client that sends extracted code snippets to the OpenRouter API and receives structured documentation responses in OpenAPI format. The plugin is equipped with a RouteParser module for automatic endpoint and parameter identification, an OpenAPIGenerator module for composing document specifications, and a SecureStorageService for API key management. Testing encompasses functional testing, OpenAPI format validation, performance testing, and documentation accuracy evaluation against manually written documentation. The results demonstrate that the developed plugin successfully automates the REST API documentation process directly within the VS Code IDE environment, reduces developer workload, and produces documentation conforming to the OpenAPI 3.1 standard with greater accuracy and consistency compared to conventional rule-based approaches.

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