Troy C. Kohwalter

SDM - An Educational Game for Software Engineering

Software Engineering is an area of computer science that focuses on practical aspects of the software production. The Undergraduate courses of Computer Science have disciplines of Software Engineering, but they are usually taught in a theoretic way and with only a few implementation exercises using the learned techniques and tools. A practical approach for the concepts studied during the Software Engineering classes would help the student in understanding the reason for using the presented concepts. Due to that, we introduce Software Development Manager, a novel simulation game where the player owns a software development company that counts with the help of a team, which is administered by the player, to develop products desired by customers. The purpose of this game is to assist in learning the knowledge of Software Engineering in a way that takes advantage of the benefits of fun and entertainment.

Game Flux Analysis with Provenance

Winning or losing a game session is the final consequence of a series of decisions and actions made during the game. The analysis and understanding of events, mistakes, and fluxes of a concrete game play may be useful for different reasons: understanding problems related to gameplay, data mining of specific situations, and even understanding educational and learning aspects in serious games. We introduce a novel approach based on provenance concepts in order to model and represent a game flux. We model the game data and map it to provenance to generate a provenance graph for analysis. As an example, we also instantiated our proposed conceptual framework and graph generation in a serious game, allowing developers and designers to identify possible mistakes and failures in gameplay design by analyzing the generated provenance graph from collected gameplay data.

Prov Viewer: A Graph-Based Visualization Tool for Interactive Exploration of Provenance Data

The analysis of provenance data for an experiment is often crucial to understand the achieved results. For long-running experiments or when provenance is captured at a low granularity, this analysis process can be overwhelming to the user due to the large volume of provenance data. In this paper we introduce, Prov Viewer, a provenance visualization tool that enables users to interactively explore provenance data. Among the visualization and exploratory features, we can cite zooming, filtering, and coloring. Moreover, we use of other properties such as shape and size to distinguish visual elements. These exploratory features are linked to the provenance semantics to ease the comprehension process. We also introduce collapsing and filtering strategies, allowing different levels of granularity exploration and analysis. We describe case studies that show how Prov Viewer has been successfully used to explore provenance in different domains, including games and urban data.

Reinforcing Software Engineering Learning through Provenance

Software engineering is focused on practical and theoretical aspects of the software production. Teaching software engineering is traditionally done through theoretical classes with some practical exercises. Recently, games and simulators were introduced as a ludic alternative for software engineering learning, where decisions and interactions become key factors to transmit and acquire knowledge. However, mistakes made by wrong decisions may jeopardize the learning process, especially when reproducing its effects is not a viable option due to the nondeterministic nature of games. With this in mind, in a previous work we proposed a novel approach based on provenance concepts in order to present the decisions and effects of such decisions when learning through games. In this work, we present an experimental evaluation of that approach with undergraduate students. The obtained results show that the use of provenance leads to faster and more accurate answers from students, including learning aspects that could not be achieved by a traditional educational game.

A Game Design Analytic System Based on Data Provenance

This paper presents a game system approach to assist game designers to make decisions and find critical points in their game through data provenance collected from a game. The proposed approach is based on generating graphs from collected data to quickly visualize the game flow. We test and validate our approach with an infinity run genre for mobile devices.

Filtering irrelevant sequential data out of game session telemetry though similarity collapses

Abstract A multitude of game sessions is started every day, generating a huge amount of data that may be useful in many different situations. For this reason, game telemetry is an important trend and feature in modern games, especially for tuning games, quality assurance, testing, and finding game aspects that need to be calibrated. The wealth of tracked information is fundamental for analysis and understanding of events, mistakes, and fluxes of a concrete game session. However, due to game dynamics, the resulting telemetry data may be overwhelming in size, making it difficult to identify sections of interest in the game session. The existing telemetry approaches normally use density clustering techniques for visual analysis, losing the temporal relationship in the process. In order to solve this problem, in this paper we propose three different similarity collapse algorithms based on the classic DBSCAN algorithm, collapsing sequential information of the graph that has similar values or represents the same states. These algorithms allow the game designer to quickly identify relevant information or state transitions without compromising the temporal sequence of events. We implemented this solution over tracked provenance data, which is graph-based, and provide two experimental studies of these algorithms using automatic experimentation and human judges to evaluate each of the proposed algorithms. These experiments show that one of the proposed algorithms is superior to DBSCAN when applied to graphs by better preserving the data semantics when collapsing provenance data. We believe that this kind of approaches will become a trend in the future process of game development.

Understanding game sessions through provenance

Abstract The outcome of a gameplay session is derived from a series of events, decisions, and interactions made during the game. Many techniques have been developed by the game industry to understand a gameplay session. A successful technique is game analytics, which aims at understanding behavior patterns to improve game quality. However, current methods are not sufficient to capture underlying cause-and-effect relationships that occur during a gameplay session, which would allow designers to better identify possible mistakes in the mechanics or fine-tune their game. Recently, it was proposed a conceptual framework based on provenance to capture these relationships. In this paper, we present a concrete framework to capture provenance data, allowing developers to add provenance gathering capabilities to their games. We instantiated our framework in two games, showing how it can be used in practice, and we developed a new game to demonstrate how provenance could be employed in early stages of game development to assist balancing the difficulty. We conducted an experiment with twelve volunteers and used the gathered provenance data to answer designers’ frequent questions when trying to understand game sessions and balancing the difficulty of their games. This supports the relevance of collecting provenance data from games.

Capturing Game Telemetry with Provenance

The outcome of a game session is derived from a series of events, decisions, and interactions that are made during the game. Many processes and techniques have been developed by the game industry in order to understand this outcome. A successful method is game analytics, which aims at understanding the player behavior patterns to improve game quality and enhance the player experience. However, the current methods for analytics are not sufficient to capture the underlying cause-and-effect influences that shape the outcome of a game session. These relationships allow developers and designers to better identify possible mistakes in the gameplay design or to fine-tune their games. In a recent work, Kohwalter et al. introduced a conceptual framework based on provenance to capture these relationships and manually instantiated such framework in some games. In this paper, we propose a concrete component for capturing provenance data and the cause-and-effect relationships among game objects, and for automatically building the correspondent provenance graph. This provenance data allows a more powerful support for the visual game analytics. We implemented our component in the Unity game engine and show two case studies over open-source games.