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

Qi, L; Song, H; Zhang, X; Srivastava, G; Xu, X; Yu, S

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

Qi, L Song, H Zhang, X Srivastava, G Xu, X Yu, S

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Journal Article
Citation:: ACM Transactions on Multimedia Computing, Communications and Applications, 2021, 17, (1s)
Issue Date:: 2021-04-01

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Field	Value	Language
dc.contributor.author	Qi, L
dc.contributor.author	Song, H
dc.contributor.author	Zhang, X
dc.contributor.author	Srivastava, G
dc.contributor.author	Xu, X
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2022-04-08T05:43:30Z
dc.date.available	2022-04-08T05:43:30Z
dc.date.issued	2021-04-01
dc.identifier.citation	ACM Transactions on Multimedia Computing, Communications and Applications, 2021, 17, (1s)
dc.identifier.issn	1551-6857
dc.identifier.issn	1551-6865
dc.identifier.uri	http://hdl.handle.net/10453/155991
dc.description.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.
dc.language	English
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	ACM Transactions on Multimedia Computing, Communications and Applications
dc.relation.isbasedon	10.1145/3417293
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Compatibility-aware web API recommendation for mashup creation via textual description mining
dc.type	Journal Article
utslib.citation.volume	17
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-04-08T05:43:26Z
pubs.issue	1s
pubs.publication-status	Published
pubs.volume	17
utslib.citation.issue	1s

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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http://hdl.handle.net/10453/155991