Compatibility-aware web API recommendation for mashup creation via textual description mining

Lianyong Qi, Houbing Song, Xuyun Zhang, Gautam Srivastava, Xiaolong Xu*, Shui Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number20
Pages (from-to)1-19
Number of pages19
JournalACM Transactions on Multimedia Computing, Communications and Applications
Volume17
Issue number1s
DOIs
Publication statusPublished - Apr 2021

Keywords

  • APIs recommendation
  • compatibility
  • mashup creation
  • Text document
  • textual description mining

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