An approach to automated videogame beta testing

Videogames developed in the 1970s and 1980s were modest programs created in a couple of months by a single person, who played the roles of designer, artist and programmer. Since then, videogames have evolved to become a multi-million dollar industry. Today, AAA game development involves hundreds of people working together over several years. Management and engineering requirements have changed at the same pace. Although many of the processes have been adapted over time, this is not quite true for quality assurance tasks, which are still done mainly manually by human beta testers due to the specific peculiarities of videogames. This paper presents an approach to automate this beta testing

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

Understanding Extended Testing Feedback

The independent (“indie”) game is a common category of video games, referring generally to those that are developed by individuals or small teams. When creating new games, most developers recruit a testing team made up of users/gamers who are not in the immediate design team and generate feedback about the game. The main objective of this study is to explore a qualitative way for categorising and filtering online reviews through social media platforms to help indie developers process user feedback efficiently during the extended game testing phase. This research adopts a qualitative methodology to develop in-depth and high-quality results based on case studies of Manifold Garden (William Chyr Studio, 2019) and No Man’s Sky (Hello Games, 2016). It includes qualitative content analysis based on Grabarczyk and Aarseth’s (2018) ontological meta-model (2018). A comparative investigation is also used to evaluate two key media platforms: YouTube and Steam. The results indicate that Steam users’ reviews focused on fundamental aspects of the game operation and game mechanics. In contrast, reviews on YouTube were related to the visual performance of games. The researcher observed an understanding gap between reviewers and developers, which means not all reviewers’ advice had been accepted. In conclusion, indie developers could consider platform types when categorising and targeting user feedback

Learning from the Past: a Process Recommendation System for Video Game Projects using Postmortems Experiences

Context: The video game industry is a billion dollar industry that faces problems in the way games are developed. One method to address these problems is using developer aid tools, such as Recommendation Systems. These tools assist developers by generating recommendations to help them perform their tasks. Objective: This article describes a systematic approach to recommend development processes for video game projects, using postmortem knowledge extraction and a model of the context of the new project, in which “postmortems” are articles written by video game developers at the end of projects, summarizing the experience of their game development team. This approach aims to provide reflections about development processes used in the game industry as well as guidance to developers to choose the most adequate process according to the contexts they’re in. Method: Our approach is divided in three separate phases: in the the first phase, we manually extracted the processes from the postmortems analysis; in the second one, we created a video game context and algorithm rules for recommendation; and finally in the third phase, we evaluated the recommended processes by using quantitative and qualitative metrics, game developers feedback, and a case study by interviewing a video game development team. Contributions: This article brings three main contributions. The first describes a database of developers’ experiences extracted from postmortems in the form of development processes. The second defines the main attributes that a video game project contain, which it uses to define the contexts of the project. The third describes and evaluates a recommendation system for video game projects, which uses the contexts of the projects to identify similar projects and suggest a set of activities in the form of a process

Videogame Intervention for Improved Control of Type 1 Diabetes in Adolescents

Type 1 diabetes is a metabolic disease characterized by an inability to produce insulin, leading to hyperglycemia and other serious complications. Type 1 diabetes can be managed with exogenous insulin and careful dietary monitoring. However, adolescents with type 1 diabetes often have difficulty adhering to optimal treatment regimens, resulting in poorer diabetic control than patients in any other age group. In this study, we will test the efficacy of a serious videogame for increasing adherence to effective treatment regimens. We hypothesize that this intervention will lead to a significant reduction in glycosylated hemoglobin A1c, the standard assessment of diabetic control. We will randomize patients to the videogame intervention or a control group and measure hemoglobin A1c levels before and after intervention. This study will evaluate an engaging, age-appropriate tool to allow clinicians to connect with adolescent patients with the goal of decreasing their incidence of future diabetes-related complications

RANCANG BANGUN GAME PLATFORMER BINTANG KECIL MENGGUNAKAN GODOT ENGINE

Entertainment industry comes in many shapes and form. One example of such things are digital video games. Despite the controversies and bad news around it, the industry shows no progress of slowing down. Many people show interest of developing video games, yet the opportunity seems abysmal, at least in this nation. This research paper intended to draw more people to the positive of video game, and possibly encourage them to try develop their own video games. Content of this paper include the basic process of developing a 2D platformer games using Godot Engine. Assets are homemade, meaning the drawing are self-made, with the exception of fonts

Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing

In this paper, we study the effects of several Monte Carlo Tree Search (MCTS) modifications for video game testing. Although MCTS modifications are highly studied in game playing, their impacts on finding bugs are blank. We focused on bug finding in our previous study where we introduced synthetic and human-like test goals and we used these test goals in Sarsa and MCTS agents to find bugs. In this study, we extend the MCTS agent with several modifications for game testing purposes. Furthermore, we present a novel tree reuse strategy. We experiment with these modifications by testing them on three testbed games, four levels each, that contain 45 bugs in total. We use the General Video Game Artificial Intelligence (GVG-AI) framework to create the testbed games and collect 427 human tester trajectories using the GVG-AI framework. We analyze the proposed modifications in three parts: we evaluate their effects on bug finding performances of agents, we measure their success under two different computational budgets, and we assess their effects on human-likeness of the human-like agent. Our results show that MCTS modifications improve the bug finding performance of the agents

Enhancing Perceived Safety in Human–Robot Collaborative Construction Using Immersive Virtual Environments

Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.University of Michigan Mcubed Grant: Virtual Prototyping of Human-Robot Collaboration in Unstructured Construction EnvironmentsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145620/1/You et al. forthcoming in AutCon.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145620/4/You et al. 2018.pdfDescription of You et al. 2018.pdf : Published Versio

Automated Video Game Testing Using Synthetic and Human-Like Agents

In this paper, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of agents -synthetic and human-like- and two distinct approaches to create them. Our agents are derived from Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) agents, but focus on finding defects. The synthetic agent uses test goals generated from game scenarios, and these goals are further modified to examine the effects of unintended game transitions. The human-like agent uses test goals extracted by our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL) algorithm from tester trajectories. MGPIRL captures multiple policies executed by human testers. These testers' aims are finding defects while interacting with the game to break it, which is considerably different from game playing. We present interaction states to model such interactions. We use our agents to produce test sequences, run the game with these sequences, and check the game for each run with an automated test oracle. We analyze the proposed method in two parts: we compare the success of human-like and synthetic agents in bug finding, and we evaluate the similarity between humanlike agents and human testers. We collected 427 trajectories from human testers using the General Video Game Artificial Intelligence (GVG-AI) framework and created three games with 12 levels that contain 45 bugs. Our experiments reveal that human-like and synthetic agents compete with human testers' bug finding performances. Moreover, we show that MGP-IRL increases the human-likeness of agents while improving the bug finding performance