Recommending Base Image for Docker Containers based on Deep Configuration Comprehension

Yinyuan Zhang, Yang Zhang, Xinjun Mao, Yiwen Wu, Bo Lin, Shangwen Wang

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%.