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Containerization allows developers to define the execution environment inwhich their software needs to be installed. Docker is the leading platform inthis field, and developers that use it are required to write a Dockerfile fortheir software. Writing Dockerfiles is far from trivial, especially when thesystem has unusual requirements for its execution environment. Despite severaltools exist to support developers in writing Dockerfiles, none of them is ableto generate entire Dockerfiles from scratch given a high-level specification ofthe requirements of the execution environment. In this paper, we present astudy in which we aim at understanding to what extent Deep Learning (DL), whichhas been proven successful for other coding tasks, can be used for thisspecific coding task. We preliminarily defined a structured natural languagespecification for Dockerfile requirements and a methodology that we use toautomatically infer the requirements from the largest dataset of Dockerfilescurrently available. We used the obtained dataset, with 670,982 instances, totrain and test a Text-to-Text Transfer Transformer (T5) model, following thecurrent state-of-the-art procedure for coding tasks, to automatically generateDockerfiles from the structured specifications. The results of our evaluationshow that T5 performs similarly to the more trivial IR-based baselines weconsidered. We also report the open challenges associated with the applicationof deep learning in the context of Dockerfile generation.

Automatically Generating Dockerfiles via Deep Learning: Challenges and Promises