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(SCC 2020) Automated Web Service Specification Generation through a Transformation-based Learning

About This Webinar

Abstract: "Web Application Programming Interface(API) allows third-party and subscribed users to access data and functions of a software application through the network or the Internet. Web APIs expose data and functions to the public users, authorized users or enterprise users. Web API providers publish API documentations to help users to understand how to interact with web-based API services, and how to use the APIs in their integration systems. The exponential raise of the number of public web service APIs may cause a challenge for software engineers to choose an efficient API. The challenge may become more complicated when web APIs updated regularly by API providers.In this paper, we introduce a novel transformation-based approach which crawls the web to collect web API documentations (unstructured documents). It generates a web API Language model from API documentations, employs different machine learning algorithms to extract information and produces a structured web API specification that compliant to Open API Specification (OAS) format. The proposed approach improves information extraction patterns and learns the variety of structured and terminologies. In our experiment, we collect a sheer number of web API documentations. Our evaluation shows that the proposed approach find RESTful API documentations with 75\% accuracy, constructs API endpoints with 84\%, constructs endpoint attributes with 95\%, and assigns endpoints to attributes with an accuracy 98\%. The proposed approach were able to produces more than 2,311 OAS web API Specifications."

Authors: Mehdi Bahrami and Wei-Peng Chen (Fujitsu Laboratory of America Inc., USA)

Email: mbahrami@fujitsu.com, wchen@us.fujitsu.com

Who can view: Everyone
Webinar Price: Free
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Mehdi Bahrami

Building the Modern Services Industry
Dr. Mehdi Bahrami was working in the Cloud Lab at the University of California, Merced. He did his Ph.D. in Electrical Engineering and Computer Science with a GPA 4.0/4.0. His Ph.D. dissertation focuses on a dynamic cloud and data privacy preservation under supervision of Professor Mukesh Singhal. He has published several technical papers in the areas of cloud computing and data privacy. He is an editor, reviewer for several international computer science journals, including Springer journals. He also served as a technical program committee member for several international IEEE computer science conferences. He is served as a featured speaker in several international conferences. He has extensive experience with software engineering and developing distributed software applications in diverse platforms. He is a recipient of the Best Demo Award at 2016 ACM ICN, 2015 Distinguished Leadership Award from Margo F. Souza Leadership Center, 2015 Margo Souza Entrepreneur in Training Award, an Achievement award from 2015 IEEE MobileCloud, and several fellowship awards during his Ph.D. study at UC Merced. He is an author/inventor of more than 30 U.S. patent applications that includes patent pending and granted patents.
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