# Toward Automatically Completing GitHub Workflows

arXiv: https://arxiv.org/abs/2308.16774 · HTML: https://mlsanity.com/paper/2308.16774/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2023.

## Built on by (most cited first, 3 of 3)

- [Investigating Execution-Aware Language Models for Code Optimization](https://mlsanity.com/paper/2503.08228/) (2025)
- [FlowScout: From Execution Feedback to Reliable Tool-Using Agent Workflows](https://mlsanity.com/paper/2608.10039/) (2026)
- [On the Impact of Code Comments for Automated Bug-Fixing: An Empirical Study](https://mlsanity.com/paper/2601.23059/) (2026)

## Abstract (arXiv)

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.