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Knowledge Graph Enhanced Third-Party Library Recommendation for Mobile Application Development
- Source :
- IEEE Access, Vol 8, Pp 42436-42446 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- With the prevalence of smart terminal devices and the rapid development of the mobile Internet, mobile application markets become increasingly prosperous. Third-party libraries have played an essential role in mobile application development. These libraries can shorten development time, increase development efficiency, and improve development quality. Currently, a large number of third-party libraries have been published, which puts a heavy burden on developers in selecting appropriate libraries. Towards this issue, in this paper, we propose a novel third-party library recommendation approach by integrating topic modeling and knowledge graph techniques. In the topic modeling component, we extract topics from textual application descriptions and make recommendations based on libraries used by applications that share similar topics with the new application to develop. In the knowledge graph component, we leverage knowledge graph to incorporate structured information of third-party libraries and applications, as well as the interaction information of applications and libraries for the recommendation. Experiments conducted on a real-world dataset show that our proposed approach outperforms several state-of-the-art approaches in terms of recommendation performance.
- Subjects :
- Topic model
Thesaurus (information retrieval)
General Computer Science
Computer science
media_common.quotation_subject
topic modeling
General Engineering
Interaction information
MKR
World Wide Web
Development (topology)
Knowledge graph
knowledge graph
Component (UML)
mobile app
Third-party library recommendation
Leverage (statistics)
General Materials Science
Quality (business)
lcsh:Electrical engineering. Electronics. Nuclear engineering
Electrical and Electronic Engineering
lcsh:TK1-9971
media_common
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....769c7116250fb0e808ea24997601f143