# Shipwright: A Human-in-the-Loop System for Dockerfile Repair

arXiv: https://arxiv.org/abs/2103.02591 · HTML: https://mlsanity.com/paper/2103.02591/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Automating Code-Related Tasks Through Transformers: The Impact of Pre-training](https://mlsanity.com/paper/2302.04048/) (2023)

## Abstract (arXiv)

Docker is a tool for lightweight OS-level virtualization. Docker images are created by performing a build, controlled by a source-level artifact called a Dockerfile. We studied Dockerfiles on GitHub, and -- to our great surprise -- found that over a quarter of the examined Dockerfiles failed to build (and thus to produce images). To address this problem, we propose SHIPWRIGHT, a human-in-the-loop system for finding repairs to broken Dockerfiles. SHIPWRIGHT uses a modified version of the BERT language model to embed build logs and to cluster broken Dockerfiles. Using these clusters and a search-based procedure, we were able to design 13 rules for making automated repairs to Dockerfiles. With the aid of SHIPWRIGHT, we submitted 45 pull requests (with a 42.2% acceptance rate) to GitHub projects with broken Dockerfiles. Furthermore, in a "time-travel" analysis of broken Dockerfiles that were later fixed, we found that SHIPWRIGHT proposed repairs that were equivalent to human-authored patches in 22.77% of the cases we studied. Finally, we compared our work with recent, state-of-the-art, static Dockerfile analyses, and found that, while static tools detected possible build-failure-inducing issues in 20.6--33.8% of the files we examined, SHIPWRIGHT was able to detect possible issues in 73.25% of the files and, additionally, provide automated repairs for 18.9% of the files.