Toward Automatically Completing GitHub Workflows

TL;DR

This paper introduces GH-WCOM, a Transformer-based system that automates the completion of GitHub workflows, significantly aiding developers in setting up CI/CD pipelines by predicting specific workflow elements.

Contribution

The paper presents a novel Transformer-based approach with an abstraction process for recommending detailed GitHub workflow components, including tool options and scripts.

Findings

GH-WCOM achieves up to 34.23% correct predictions.

Model confidence correlates with recommendation correctness.

Supports automation in CI/CD pipeline setup.

Abstract

Continuous integration and delivery (CI/CD) are nowadays at the core of software development. Their benefits come at the cost of setting up and maintaining the CI/CD pipeline, which requires knowledge and skills often orthogonal to those entailed in other software-related tasks. While several recommender systems have been proposed to support developers across a variety of tasks, little automated support is available when it comes to setting up and maintaining CI/CD pipelines. We present GH-WCOM (GitHub Workflow COMpletion), a Transformer-based approach supporting developers in writing a specific type of CI/CD pipelines, namely GitHub workflows. To deal with such a task, we designed an abstraction process to help the learning of the transformer while still making GH-WCOM able to recommend very peculiar workflow elements such as tool options and scripting elements. Our empirical study shows that GH-WCOM provides up to 34.23% correct predictions, and the model's confidence is a reliable proxy for the recommendations' correctness likelihood.