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Compatibility-Aware Web API Recommendation for Mashup Creation via Textual Description Mining

Published: 31 March 2021 Publication History

Abstract

With the ever-increasing prosperity of web Application Programming Interface (API) sharing platforms, it is becoming an economic and efficient way for software developers to design their interested mashups through web API re-use. Generally, a software developer can browse, evaluate, and select his or her preferred web APIs from the API's sharing platforms to create various mashups with rich functionality. The big volume of candidate APIs places a heavy burden on software developers’ API selection decisions. This, in turn, calls for the support of intelligent API recommender systems. However, existing API recommender systems often face two challenges. First, they focus more on the functional accuracy of APIs while neglecting the APIs’ actual compatibility. This then creates incompatible mashups. Second, they often require software developers to input a set of keywords that can accurately describe the expected functions of the mashup to be developed. This second challenge tests partial developers who have little background knowledge in the fields. To tackle the above-mentioned challenges, in this article we propose a compatibility-aware and text description-driven web API recommendation approach (named WARtext). WARtext guarantees the compatibility among the recommended APIs by utilizing the APIs’ composition records produced by historical mashup creations. Besides, WARtext entitles a software developer to type a simple text document that describes the expected mashup functions as input. Then through textual description mining, WARtext can precisely capture the developers’ functional requirements and then return a set of APIs with the highest compatibility. Finally, through a real-world mashup dataset ProgrammableWeb, we validate the feasibility of our novel approach.

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 17, Issue 1s
January 2021
353 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/3453990
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 31 March 2021
Accepted: 01 August 2020
Revised: 01 July 2020
Received: 01 March 2020
Published in TOMM Volume 17, Issue 1s

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Author Tags

  1. Text document
  2. textual description mining
  3. APIs recommendation
  4. compatibility
  5. mashup creation

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  • Research-article
  • Refereed

Funding Sources

  • National Natural Science Foundation of China
  • Natural Science Foundation of Shandong Province
  • Open Project of State Key Laboratory for Novel Software Technology
  • Fundamental Research Funds for the Central Universities

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  • (2024)PRKG: Pre-Training Representation and Knowledge-Graph-Enhanced Web Service Recommendation for Mashup CreationIEEE Transactions on Network and Service Management10.1109/TNSM.2024.335199921:2(1737-1749)Online publication date: 10-Jan-2024
  • (2024)Poisoning QoS-aware cloud API recommender system with generative adversarial network attackExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121630238:PBOnline publication date: 27-Feb-2024
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  • (2023)Diversity-driven automated web API recommendation based on implicit requirementsApplied Soft Computing10.1016/j.asoc.2023.110137136:COnline publication date: 1-Mar-2023
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