In the rapidly evolving landscape of web services, the significance of efficiently designed and well-documented APIs is paramount. In this paper, we present APIstic an API analytics dataset and exploration tool to navigate and segment APIs based on an extensive set of precomputed metrics extracted from OpenAPI specifications, sourced from GitHub, SwaggerHub, BigQuery and APIs.guru.
These pre-computed metrics are categorized into structure, data model, natural language description, and security metrics.
The extensive dataset of varied API metrics provides crucial insights into API design and documentation for both researchers and practitioners. Researchers can use APIstic as an empirical resource to extract refined samples, analyze API design trends, best practices, smells, and patterns. For API designers, it serves as a benchmarking tool to assess, compare, and improve API structures, data models, and documentation using metrics to select points of references among 1,275,568 valid OpenAPI specifications.
The paper discusses potential use cases of the collected data and presents a descriptive analysis of selected API analytics metrics.