Auto-Generating Comprehensive API Documentation Using Large Language Models in PaaS

Abstract

The rapid evolution of cloud-based Platform-as-a-Service (PaaS) solutions has intensified the need for comprehensive, user-friendly, and dynamic API documentation that caters to diverse developer needs. Traditional methods of generating API documentation are often time-intensive and static, failing to accommodate the complexities and real-time requirements of modern software development. Large Language Models (LLMs), renowned for their contextual understanding and generative capabilities, present a transformative solution to these challenges. This research investigates the application of LLMs in auto-generating comprehensive API documentation, emphasizing their potential to produce interactive and dynamic documentation, Software Development Kits (SDKs), and real-time code samples.

The paper begins by exploring the theoretical underpinnings of LLMs, detailing their architecture, training paradigms, and capabilities. Emphasis is placed on state-of-the-art transformer models, including GPT-based systems, which leverage billions of parameters to generate human-like, context-aware text. The study examines the specific advantages of employing LLMs for API documentation, such as their ability to interpret unstructured API schemas, generate consistent and error-free documentation, and customize outputs based on user requirements. Additionally, the integration of LLMs into PaaS environments is analyzed, demonstrating how these models can be seamlessly deployed using fine-tuning techniques, custom prompt engineering, and real-time inference pipelines.

A critical component of the research is the development of an implementation framework for employing LLMs in API documentation generation. This framework involves three key phases: input processing, generative modeling, and output optimization. The input processing phase focuses on the extraction and preprocessing of API metadata, including specifications written in OpenAPI, Swagger, or RAML formats. Subsequently, LLMs generate documentation enriched with dynamic elements, such as live code snippets, usage scenarios, and contextual annotations. These outputs are then refined using reinforcement learning techniques, such as Reinforcement Learning with Human Feedback (RLHF), to enhance the relevance and usability of the generated content.

To validate the proposed framework, a comprehensive evaluation is conducted using multiple APIs across various domains, including cloud services, data analytics, and machine learning platforms. Performance metrics, including accuracy, coherence, and developer satisfaction, are analyzed to assess the efficacy of LLM-generated documentation. Results indicate that LLMs outperform traditional methods by producing highly interactive and contextually accurate documentation, reducing the time-to-market for SDKs, and significantly improving developer productivity.

The study also addresses critical challenges in implementing LLM-driven documentation generation. Issues such as model biases, computational costs, and data privacy concerns are examined, and potential mitigation strategies are proposed. Furthermore, the scalability of this approach in handling complex, high-volume API ecosystems is discussed. Future directions in the domain are highlighted, emphasizing advancements in LLM architectures, the integration of multimodal capabilities, and the adoption of federated learning paradigms to further enhance documentation quality and accessibility.