# World of Bugs: A Platform for Automated Bug Detection in 3D Video Games

arXiv: https://arxiv.org/abs/2206.11037 · HTML: https://mlsanity.com/paper/2206.11037/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 21 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Augmenting Automated Game Testing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.15819/) (2021)
- [Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents](https://mlsanity.com/paper/2103.13798/) (2021)
- [Automated Video Game Testing Using Synthetic and Human-Like Agents](https://mlsanity.com/paper/1906.00317/) (2019)
- [Navigation and Exploration in 3D-Game Automated Play Testing](https://mlsanity.com/paper/2009.07015/) (2020)

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

- [Weak Supervision for Label Efficient Visual Bug Detection](https://mlsanity.com/paper/2309.11077/) (2023)
- [Automatic Bug Detection in Games using LSTM Networks](https://mlsanity.com/paper/2312.08418/) (2023)
- [Evaluating VLMs for Autonomous Agent-Driven Geometry Clipping Detection in Video Game QA](https://mlsanity.com/paper/2607.25921/) (2026)

## Abstract (arXiv)

We present World of Bugs (WOB), an open platform that aims to support Automated Bug Detection (ABD) research in video games. We discuss some open problems in ABD and how they relate to the platform's design, arguing that learning-based solutions are required if further progress is to be made. The platform's key feature is a growing collection of common video game bugs that may be used for training and evaluating ABD approaches.