Measuring Visual Consistency in 3D Rendering Systems

One of the major challenges facing a present day game development company is the removal of bugs from such complex virtual environments. This work presents an approach for measuring the correctness of synthetic scenes generated by a rendering system of a 3D application, such as a computer game. Our approach builds a database of labelled point clouds representing the spatiotemporal colour distribution for the objects present in a sequence of bug-free frames. This is done by converting the position that the pixels take over time into the 3D equivalent points with associated colours. Once the space of labelled points is built, each new image produced from the same game by any rendering system can be analysed by measuring its visual inconsistency in terms of distance from the database. Objects within the scene can be relocated (manually or by the application engine); yet the algorithm is able to perform the image analysis in terms of the 3D structure and colour distribution of samples on the surface of the object. We applied our framework to the publicly available game RacingGame developed for Microsoft(R) Xna(R). Preliminary results show how this approach can be used to detect a variety of visual artifacts generated by the rendering system in a professional quality game engine

ANALYSIS OF ARTIFICIAL INTELLIGENCE APPLICATIONS FOR AUTOMATED TESTING OF VIDEO GAMES

Game testing is a software testing process for quality control in video games. Game environments, sometimes called levels or maps, are complex and interactive systems. These environments can include level geometry, interactive entities, player and non-player controllable characters etc. Depending on the number and complexity of levels, testing them by hand may take a considerable effort. This is especially true for video games with procedurally generated levels that are automatically created using a specifically designed algorithm. A single change in a procedural generation algorithm can alter all of the video game levels, and they will have to be retested to ensure they are still completable or meet any other requirements of the game. This task may be suitable for automation, in particular using Artificial Intelligence (AI). The goal of this paper is to explore the most promising and up-to-date research on AI applications for video game testing to serve as a reference for anyone starting in the field

Automating Game Progression to Empower Users with Disabilities

With roughly 67% of Americans playing video games [about 211 million people], gaming is an integral part of American culture (Crecente). In the United States, however, “perhaps 2 percent of the population [about 6.3 million people] cannot play computer games because of a disability, and 9 percent [about 28.4 million] can play only at a reduced level” (Garber). Games built for those with disabilities fail in two primary ways: many are designed with educational goals, despite research which shows disabled people want recreational games and they’re designed for very young children, though gaming is most popular in the age range of 18 to 24 (Morris). This thesis stemmed from a desire to create an enjoyable game for this significantly underserved population.Bachelor of Scienc

A framework for the semi-automatic testing of video games

Game environments are complex interactive systems that require extensive analysis and testing to ensure that they are at a high enough quality to be released commercially. In particular, the last build of the product needs an additional and extensive beta test carried out by people that play the game in order to establish its robustness and playability. This entails additional costs from the viewpoint of a company as it requires the hiring of play testers. In the present work we propose a general software framework that integrates Artificial Intelligence (AI) Agents and Computer Vision (CV) technologies to support the test team and help to improve and accelerate the test process. We also present a prototype shadow alias detection algorithm that illustrates the effectiveness of the framework in developing automated visual debugging technology that will ease the heavy cost of beta testing games

A Framework for the Semi-Automatic Testing of Video Games

Game environments are complex interactive systems that require extensive analysis and testing to ensure that they are at a high enough quality to be released commercially. In particular, the last build of the product needs an additional and extensive beta test carried out by people that play the game in order to establish its robustness and playability. This entails additional costs from the viewpoint of a company as it requires the hiring of play testers. In the present work we propose a general software framework that integrates Artificial Intelligence (AI) Agents and Computer Vision (CV) technologies to support the test team and help to improve and accelerate the test process. We also present a prototype shadow alias detection algorithm that illustrates the effectiveness of the framework in developing automated visual debugging technology that will ease the heavy cost of beta testing games