Modern AAA video games feature huge game levels and maps which are increasingly hard for level testers to cover exhaustively. As a result, games often ship with catastrophic bugs such as the player falling through the ﬂoor or being stuck in walls. We propose an approach speciﬁcally targeted at reachability bugs in simulated 3D environments based on the powerful exploration algorithm, Go-Explore, which saves unique checkpoints across the map and then identiﬁes promising ones to explore from. We show that when coupled with simple heuristics derived from the game’s navigation mesh, Go-Explore ﬁnds challenging bugs and comprehen- sively explores complex environments without the need for human demonstration or knowledge of the game dynamics . Go-Explore vastly outperforms more complicated baselines including reinforcement learning with intrinsic curiosity in both covering the navigation mesh and number of unique po- sitions across the map discovered. Finally, due to our use of parallel agents, our algorithm can fully cover a vast 1.5km x 1.5km game world within 10 hours on a single machine making it extremely promising for continuous testing suites.

pdf/go-explore-complex-3d-game-environments-for-automated-mval1ubc.pdf

Go-Explore Complex 3D Game Environments for Automated Reachability Testing

As the complexity of video games continues to evolve, so does the importance of effective game testing methodologies. To this end, automated game testing has emerged as a pivotal approach to ensure the quality and functionality of modern games. The objective of the present paper is to identify, through a literature review and the application of Open and Axial coding, the most commonly analysed and mentioned issues in automated game testing literature. The results of the study provide a taxonomy of 26 different issues that are assessed in the software engineering literature by automated game testing practice, grouped in five higher-level categories. The elicited taxonomy can serve as an instrument for game testers, researchers and tool developers to evaluate testing approaches and techniques, enable comparability of research results, and design instruments to investigate functional aspects of games in development.

Know Your Bugs: A Survey of Issues in Automated Game Testing Literature

As video games evolve into expansive, detailed worlds, visual quality becomes essential, yet increasingly challenging. Traditional testing methods, limited by resources, face difficulties in addressing the plethora of potential bugs. Machine learning offers scalable solutions; however, heavy reliance on large labeled datasets remains a constraint. Addressing this challenge, we propose a novel method, utilizing unlabeled gameplay and domain-specific augmentations to generate datasets & self-supervised objectives used during pre-training or multi-task settings for downstream visual bug detection. Our methodology uses weak-supervision to scale datasets for the crafted objectives and facilitates both autonomous and interactive weak-supervision, incorporating unsupervised clustering and/or an interactive approach based on text and geometric prompts. We demonstrate on first-person player clipping/collision bugs (FPPC) within the expansive Giantmap game world, that our approach is very effective, improving over a strong supervised baseline in a practical, very low-prevalence, low data regime (0.336 $\rightarrow$ 0.550 F1 score). With just 5 labeled "good" exemplars (i.e., 0 bugs), our self-supervised objective alone captures enough signal to outperform the low-labeled supervised settings. Building on large-pretrained vision models, our approach is adaptable across various visual bugs. Our results suggest applicability in curating datasets for broader image and video tasks within video games beyond visual bugs. 

pdf/weak-supervision-for-label-efficient-visual-bug-detection-f67vebtikt.pdf

Weak Supervision for Label Efficient Visual Bug Detection

The capability to automatically explore different possible game states and functionality is valuable for the automated test and analysis of computer games. However, automatic exploration requires an exploration agent to be capable of determining and performing the possible actions in game states, for which a model is typically unavailable in games built with traditional game engines. Therefore, existing work on automatic exploration typically either manually defines a game's action space or imprecisely guesses the possible actions. In this paper we propose a program analysis technique compatible with traditional game engines, which automatically analyzes the user input handling logic present in a game to determine a discrete action space corresponding to the possible user inputs, along with the conditions under which the actions are valid, and the relevant user inputs to simulate on the game to perform a chosen action. We implemented a prototype of our approach capable of producing the action spaces of Gym environments for Unity games, then evaluated the exploration performance enabled by our technique for random exploration and exploration via curiosity-driven reinforcement learning agents. Our results show that for most games, our analysis enables exploration performance that matches or exceeds that of manually engineered action spaces, and the analysis is fast enough for real time game play.

Automatically Defining Game Action Spaces for Exploration Using Program Analysis

In strategy games, one of the most important aspects of game design is maintaining a sense of challenge for players. Many mobile titles feature quick gameplay loops that allow players to progress steadily, requiring an abundance of levels and puzzles to prevent them from reaching the end too quickly. As with any content creation, testing and validation are essential to ensure engaging gameplay mechanics, enjoyable game assets, and playable levels. In this paper, we propose an automated approach that can be leveraged for gameplay testing and validation that combines traditional scripted methods with reinforcement learning, reaping the benefits of both approaches while adapting to new situations similarly to how a human player would. We test our solution on a popular tower defense game, Plants vs. Zombies. The results show that combining a learned approach, such as reinforcement learning, with a scripted AI produces a higher-performing and more robust agent than using only heuristic AI, achieving a 57.12% success rate compared to 47.95% in a set of 40 levels. Moreover, the results demonstrate the difficulty of training a general agent for this type of puzzle-like game. 

pdf/reinforcement-learning-for-high-level-strategic-control-in-4kgod298bo.pdf

Reinforcement Learning for High-Level Strategic Control in Tower Defense
  Games

Deep reinforcement learning (DRL) has attracted much attention in automated game testing. Early attempts rely on game internal information for game space exploration, thus requiring deep integration with games, which is inconvenient for practical applications. In this work, we propose using only screenshots/pixels as input for automated game testing and build a general game testing agent, Inspector, that can be easily applied to different games without deep integration with games. In addition to covering all game space for testing, our agent also tries to take human-like behaviors to interact with key objects in a game, since some bugs usually happen in player-object interactions. Inspector is based on purely pixel inputs and comprises three key modules: game space explorer, key object detector, and human-like object investigator. Game space explorer aims to explore the whole game space by using a curiosity-based reward function with pixel inputs. Key object detector aims to detect key objects in a game, based on a small number of labeled screenshots. Human-like object investigator aims to mimic human behaviors for investigating key objects via imitation learning. We conduct experiments on two popular video games: Shooter Game and Action RPG Game1. Experiment results demonstrate the effectiveness of Inspector in exploring game space, detecting key objects, and investigating objects. Moreover, Inspector successfully discovers two potential bugs in those two games. The demo video of Inspector is available at https://github.com/Inspector-GameTestinyInspector-GameTesting.

Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation

The game industry is challenged to cope with increasing growth in demand and game complexity while maintaining acceptable quality standards for released games. Classic approaches solely depending on human efforts for quality assurance and game testing do not scale effectively in terms of time and cost. Game-testing AI agents that learn by interaction with the environment have the potential to mitigate these challenges with good scalability properties on time and costs. However, most recent work in this direction depends on game state information for the agent’s state representation, which limits generalization across different game scenarios. Moreover, game test engineers usually prefer exploring a game in a specific style, such as exploring the golden path, yet, current game testing AI agents do not provide an explicit way to satisfy such a preference. This paper addresses these limitations by proposing an agent design that mainly depends on pixel-based state observations while exploring the environment conditioned on a user’s preference specified by demonstration trajectories. In addition, we propose an imitation learning method that couples self-supervised and supervised learning objectives to enhance the quality of imitation behaviors. Our agent significantly outperforms state-of-the-art pixel-based game testing agents over exploration coverage and test execution quality when evaluated on a complex open-world environment resembling many aspects of real AAA games.

Preference-conditioned Pixel-based AI Agent For Game Testing

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Adrian Brown

Author Tools

Chat about Author

Papers

Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation

Preference-conditioned Pixel-based AI Agent For Game Testing