Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (5): 957-971.doi: 10.1007/s11390-019-1954-4

Special Issue: Data Management and Data Mining; Software Systems

• Special Section on Software Systems 2019 • Previous Articles     Next Articles

Semi-Supervised Learning Based Tag Recommendation for Docker Repositories

Wei Chen1,2, Member, CCF, Jia-Hong Zhou1,2, Jia-Xin Zhu1,2, Member, CCF, Guo-Quan Wu1,2,3, Member, CCF, Jun Wei1,2,3, Member, CCF   

  1. 1 Institute of Software, Chinese Academy of Sciences, Beijing 100190, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China;
    3 State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2019-02-28 Revised:2019-07-12 Online:2019-08-31 Published:2019-08-31
  • About author:Wei Chen received his Ph.D. degree in computer software and theory from Institute of Software, Chinese Academy of Sciences, Beijing, in 2013. He is currently an associate professor in Institute of Software, Chinese Academy of Sciences, Beijing. He is a member of CCF. His research interests include service-oriented computing, cloud computing and DevOps.
  • Supported by:
    This work was supported by the National Natural Key Research and Development Program of China under Grant No. 2016YFB1000803, and the National Natural Science Foundation of China under Grant Nos. 61732019 and 61572480.

Docker has been the mainstream technology of providing reusable software artifacts recently. Developers can easily build and deploy their applications using Docker. Currently, a large number of reusable Docker images are publicly shared in online communities, and semantic tags can be created to help developers effectively reuse the images. However, the communities do not provide tagging services, and manually tagging is exhausting and time-consuming. This paper addresses the problem through a semi-supervised learning-based approach, named SemiTagRec. SemiTagRec contains four components:(1) the predictor, which calculates the probability of assigning a specific tag to a given Docker repository; (2) the extender, which introduces new tags as the candidates based on tag correlation analysis; (3) the evaluator, which measures the candidate tags based on a logistic regression model; (4) the integrator, which calculates a final score by combining the results of the predictor and the evaluator, and then assigns the tags with high scores to the given Docker repositories. SemiTagRec includes the newly tagged repositories into the training data for the next round of training. In this way, SemiTagRec iteratively trains the predictor with the cumulative tagged repositories and the extended tag vocabulary, to achieve a high accuracy of tag recommendation. Finally, the experimental results show that SemiTagRec outperforms the other approaches and SemiTagRec's accuracy, in terms of Recall@5 and Recall@10, is 0.688 and 0.781 respectively.

Key words: tag recommendation; Docker repository; Dockerfile; semi-supervised learning;

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