# Automatic Bug Detection in Games using LSTM Networks

arXiv: https://arxiv.org/abs/2312.08418 · HTML: https://mlsanity.com/paper/2312.08418/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 13 Dec 2023.

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

- [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)
- [Learning to Identify Perceptual Bugs in 3D Video Games](https://mlsanity.com/paper/2202.12884/) (2022)
- [World of Bugs: A Platform for Automated Bug Detection in 3D Video Games](https://mlsanity.com/paper/2206.11037/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Hybrid Co-Finetuning Approach for Visual Bug Detection in Video Games](https://mlsanity.com/paper/2510.03591/) (2025)

## Abstract (arXiv)

We introduced a new framework to detect perceptual bugs using a Long Short-Term Memory (LSTM) network, which detects bugs in video games as anomalies. The detected buggy frames are then clustered to determine the category of the occurred bug. The framework was evaluated on two First Person Shooter (FPS) games. Results show the effectiveness of the framework.