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- W4286331454 abstract "Docker containers are being widely used in large-scale industrial environments. In practice, developers must manually specify the base image in the dockerfile in the process of container creation. However, finding the proper base image is a nontrivial task because manually searching is time-consuming and easily leads to the use of unsuitable base images, especially for newcomers. There is still a lack of automatic approaches for recommending related base image for developers through dockerfile configuration. To tackle this problem, this paper makes the first attempt to propose a neural network approach named DCCimagerec which is based on deep configuration comprehension. It aims to use the structural configuration features of dockerfile extracted by AST and path-attention model to recommend potentially suitable base image. The evaluation experiments based on about 83,000 dockerfiles show that DCCimagerec outperforms multiple baselines, improving Precision by 7.5%-67.5%, Recall by 6.2%-106.6%, and F1 by 7.5%-150.2%." @default.
- W4286331454 created "2022-07-21" @default.
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- W4286331454 date "2022-03-01" @default.
- W4286331454 modified "2023-10-12" @default.
- W4286331454 title "Recommending Base Image for Docker Containers based on Deep Configuration Comprehension" @default.
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- W4286331454 doi "https://doi.org/10.1109/saner53432.2022.00060" @default.
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