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  • 2020.2.12 Data-Driven Web APIs Recommendation for Building Web Applications

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Data-Driven Web APIs Recommendation for Building Web Applications

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
  • L. Qi
  • Q. He
  • F. Chen
  • X. Zhang
  • W. Dou
  • Q. Ni
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<mark>Journal publication date</mark>24/02/2020
<mark>Journal</mark>IEEE Transactions on Big Data
Number of pages15
Publication StatusE-pub ahead of print
Early online date24/02/20
<mark>Original language</mark>English

Abstract

The ever-increasing popularity of web APIs allows app developers to leverage a set of existing APIs to achieve their sophisticated objectives. The heavily fragmented distribution of web APIs makes it challenging for an app developer to find appropriate and compatible web APIs. Currently, app developers usually have to manually discover candidate web APIs, verify their compatibility and select appropriate and compatible ones. This process is cumbersome and requires detailed knowledge of web APIs which is often too demanding. It has become a major obstacle to further and broader applications of web APIs. To address this issue, we first propose a web API correlation graph built on extensive data about the compatibility between web APIs. Then, we propose WAR (Web APIs Recommendation), the first data-driven approach for web APIs recommendation that integrates API discovery, verification and selection operations based on keywords search over the web API correlation graph. WAR assists app developers without detailed knowledge of web APIs in searching for appropriate and compatible APIs by typing a few keywords that represent the tasks required to achieve app developers’ objectives. We conducted large-scale experiments on 18,478 real-world APIs and 6,146 real-world apps to demonstrate the usefulness and efficiency of WAR.

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©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.