![]() ![]() AR Manu, Jitendra Kumar Patel, Shakil Akhtar, VK Agrawal, and KN Bala Subramanya Murthy.On using machine learning to automatically classify software applications into domain categories. Mario Linares-Vásquez, Collin McMillan, Denys Poshyvanyk, and Mark Grechanik.Mudablue: An automatic categorization system for open source repositories. Shinji Kawaguchi, Pankaj K Garg, Makoto Matsushita, and Katsuro Inoue.2017 Annual Container Adoption Survey: Huge Growth in Containers. In ACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing. Testing idempotence for infrastructure as code. Waldemar Hummer, Florian Rosenberg, Fábio Oliveira, and Tamar Eilam.David W Hosmer Jr, Stanley Lemeshow, and Rodney X Sturdivant.Topic Suggestions for Millions of Repositories. In Proceedings of the 14th International Conference on Mining Software Repositories. An empirical analysis of the Docker container ecosystem on GitHub. Jürgen Cito, Gerald Schermann, John Erik Wittern, Philipp Leitner, Sali Zumberi, and Harald C Gall.In Computer Software and Applications Conference (COMPSAC), 2017 IEEE 41st Annual, Vol. A Hierarchical Categorization Approach for Configuration Management Modules. Wei Chen, Peixing Xu, Wensheng Dou, Guoquan Wu, Chushu Gao, and Jun Wei.In Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS), International Conference on. On The Evaluation of Security Properties of Containerized Systems. In Computer Software and Applications Conference (COMPSAC), 2016 IEEE 40th Annual, Vol. Greta: Graph-based tag assignment for github repositories. Xuyang Cai, Jiangang Zhu, Beijun Shen, and Yuting Chen.Journal of Machine Learning Research 13, Feb (2012), 281-305. Random search for hyper-parameter optimization. In Software Maintenance (ICSM), 2010 IEEE International Conference on. Fuzzy set approach for automatic tagging in evolving software. Jafar M Al-Kofahi, Ahmed Tamrawi, Tung Thanh Nguyen, Hoan Anh Nguyen, and Tien N Nguyen.In addition, D-Tagger outperforms the state-of-the-art approach when tagging repositories without description documents. The experimental results show that the accuracy of D-Tagger, in terms of and, achieve 0.675 and 0.712 respectively. We evaluate D-Tagger on over 100,000 repositories of Docker Hub (accessed until Aug. D-Tagger finally makes a combination by considering both of the two perspectives. When regarding Dockerfile as configuration code, D-Tagger constructs a feature model based on key instructions that identify the Dockerfile, and then recommends tags with a similarity-based ranking method. When taking Dockerfile as specific description, D-Tagger models a repository with its labeled tags and the terms extracted from its Dockerfile, and employs Labeled Latent Dirichlet Allocation algorithm to make tag recommendation. Thus, based on Dockerfile analysis, this paper proposes D-Tagger, a tag recommendation approach to addressing the problem of multi-labeling Docker repositories. However, in Docker Hub, tags are not well supported to semantically describing the repositories, and manually tagging is still an exhausting and time-consuming task.ĭockerfile specifies Docker repository in a rigorous and compact way. Given a huge number of Docker repositories, tag recommendation is essential to ensure that relevant ones can be easily retrieved, because tagging is practical in describing, bookmarking, navigating and searching software objects. Docker repositories usually contain Docker images and Dockerfiles, where Docker images are a kind of off-the-shelf artifact and Dockerfiles specify how to automatically build Docker images following the notion of Infrastructure-as-Code. ![]()
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