# A Survey of Video Game Testing

arXiv: https://arxiv.org/abs/2103.06431 · HTML: https://mlsanity.com/paper/2103.06431/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2021.

## Built on by (most cited first, 18 of 18)

- [CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning](https://mlsanity.com/paper/2203.11096/) (2022)
- [Large Language Models are Pretty Good Zero-Shot Video Game Bug Detectors](https://mlsanity.com/paper/2210.02506/) (2022)
- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [GameGen-Verifier: Parallel Keypoint-Based Verification for LLM-Generated Games via Runtime State Injection](https://mlsanity.com/paper/2605.07442/) (2026)
- [Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation](https://mlsanity.com/paper/2207.08379/) (2022)
- [Learning to Identify Perceptual Bugs in 3D Video Games](https://mlsanity.com/paper/2202.12884/) (2022)
- [LLMs May Not Be Human-Level Players, But They Can Be Testers: Measuring Game Difficulty with LLM Agents](https://mlsanity.com/paper/2410.02829/) (2024)
- [Leveraging LLM Agents for Automated Video Game Testing](https://mlsanity.com/paper/2509.22170/) (2025)
- [Robust Imitation Learning for Automated Game Testing](https://mlsanity.com/paper/2401.04572/) (2024)
- [Agents in the Sandbox: End-to-End Crash Bug Reproduction for Minecraft](https://mlsanity.com/paper/2503.20036/) (2025)
- [Go-Explore Complex 3D Game Environments for Automated Reachability Testing](https://mlsanity.com/paper/2209.00570/) (2022)
- [Preference-conditioned Pixel-based AI Agent For Game Testing](https://mlsanity.com/paper/2308.09289/) (2023)
- [Level Up Your Tutorials: VLMs for Game Tutorials Quality Assessment](https://mlsanity.com/paper/2408.08396/) (2024)
- [Human-AI Collaborative Game Testing with Vision Language Models](https://mlsanity.com/paper/2501.11782/) (2025)
- [Knowledge Graph-enhanced Large Language Model for Incremental Game PlayTesting](https://mlsanity.com/paper/2511.02534/) (2025)
- [Combining Neuroevolution with the Search for Novelty to Improve the Generation of Test Inputs for Games](https://mlsanity.com/paper/2407.04985/) (2024)
- [CA2: Code-Aware Agent for Automated Game Testing](https://mlsanity.com/paper/2605.13918/) (2026)
- [TempGlitch: Evaluating Vision-Language Models for Temporal Glitch Detection in Gameplay Videos](https://mlsanity.com/paper/2605.21443/) (2026)

## Abstract (arXiv)

Video-game projects are notorious for having day-one bugs, no matter how big their budget or team size. The quality of a game is essential for its success. This quality could be assessed and ensured through testing. However, to the best of our knowledge, little is known about video-game testing. In this paper, we want to understand how game developers perform game testing. We investigate, through a survey, the academic and gray literature to identify and report on existing testing processes and how they could automate them. We found that game developers rely, almost exclusively, upon manual play-testing and the testers' intrinsic knowledge. We conclude that current testing processes fall short because of their lack of automation, which seems to be the natural next step to improve the quality of games while maintaining costs. However, the current game-testing techniques may not generalize to different types of games.