Core-reviewer recommendation based on Pull Request topic model and collaborator social network-Reference-Cited by-同舟云学术

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Core-reviewer recommendation based on Pull Request topic model and collaborator social network

Published:2019-07-15 Issue:8 Volume:24 Page:5683-5693
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Liao Zhifang^ORCID,Wu ZeXuan,Li Yanbing,Zhang Yan,Fan Xiaoping,Wu Jinsong

Funder

National Natural Science Foundation of China

China Association for Science and Technology

Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology

Hunan Provincial Science and Technology Department

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00500-019-04217-7.pdf

Reference34 articles.

1. Balachandran V (2013) Reducing human effort and improving quality in peer code reviews using automatic static analysis and reviewer recommendation. In: Proceedings ICSE’13, pp 931–940

2. Begel A, Bosch J, Storey M-A (2013) Social networking meets software development: perspectives from GitHub, msdn, stack exchange, and top coder. IEEE Softw 30(1):52–66

3. Bian J, Jiang Z, Chen Q (2014) Research on multi-document summarization based on LDA topic model. In: Proceedings 2014 Sixth international conference on intelligent human-machine systems and cybernetics

4. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

5. Chang X, Ma Z, Lin M, Yang Y, Hauptmann AG (2017) Feature interaction augmented sparse learning for fast kinect motion detection. IEEE Trans Image Process 26(8):3911–3920

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