Recommending Base Image for Docker Containers based on Deep Configuration Comprehension
2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)(2022)
摘要
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%.
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关键词
Docker container,Base image,AST
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