WATAPI: Composing Web API Specification from API Documentations through an Intelligent and Interactive Annotation Tool
Mehdi Bahrami, Weipeng Chen · 2019
The number of web APIs grows rapidly. Each API provider offers API documentations which comes with diversity and complexity of structures. In order to understand a large number of diverse web APIs, we employ deep learning that extracts key objects of a large number of web APIs and produce a unified API specification (Open API Specification). However, the unified API specification is not a simple task due to heterogeneous API documentations and it is required additional human evaluation to adjust incorrect information which produced by machine. In this paper, we introduce WATAPI which represents an automation tool for representing machine learning outcomes and adding a user as a humanin-the-loop to transparently interact with complex machinelearning components to process diverse API documentations. WATAPI allows the user to annotate API documentations and/or adjust the automated annotation process which produced by machine-learning models' predictions. WATAPI is also capable to perform as a semi-automated annotation tool where it records user's interactions. WATAPI is able to automatically apply user's interaction of one API annotation to another API documentations with a similar structure. The user's annotation adjustment provides feedback to machinelearning components for improving the accuracy of extracting Open API Specifications.