Game Engine Conventions and Games that Challenge them: Subverting Conventions as Metacommentary

Consumer-grade game engines such as Multimedia Fusion and RPG Maker have dramatically extended the reach of digital games as a medium. They have also spawned online communities, where conventions and canons of using these tools have evolved. These partly stem from the functional constraints of the game engines themselves and are institutionalized through manuals, examples, tutorials, and games made with them. However, some members of game engine communities actively seek to challenge these conventions by experimenting with the engines and finding ingenious ways to put them to unexpected uses. Such experiments can be regarded as a form of metacommentary on the engines’ capabilities and limitations. While arguably impractical and inefficient, they enrich the scope of what can be done with the engine and can contribute to its further development

Advanced Artificial Intelligence Technology Testbed

The Advanced Artificial Intelligence Technology Testbed (AAITT) is a laboratory testbed for the design, analysis, integration, evaluation, and exercising of large-scale, complex, software systems, composed of both knowledge-based and conventional components. The AAITT assists its users in the following ways: configuring various problem-solving application suites; observing and measuring the behavior of these applications and the interactions between their constituent modules; gathering and analyzing statistics about the occurrence of key events; and flexibly and quickly altering the interaction of modules within the applications for further study

Let's mix it up: interviews exploring the practical and technical challenges of interactive mixing in games

Game audio has come a long way since the simple electronic beeps of the early 1970s, when significant technical constraints governed the scope of creative possibilities. Recent years have witnessed technological advancements on an unprecedented scale; no sooner is one technology introduced than it is superseded by another, boasting a range of new refinements and enhanced performance

Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games

In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.Comment: CHI'1

A taxonomy of approaches for integrating attack awareness in applications

Software applications are subject to an increasing number of attacks, resulting in data breaches and financial damage. Many solutions have been considered to help mitigate these attacks, such as the integration of attack-awareness techniques. In this paper, we propose a taxonomy illustrating how existing attack awareness techniques can be integrated into applications. This work provides a guide for security researchers and developers, aiding them when choosing the approach which best fits the needs of their application

The effects of change decomposition on code review -- a controlled experiment

Background: Code review is a cognitively demanding and time-consuming process. Previous qualitative studies hinted at how decomposing change sets into multiple yet internally coherent ones would improve the reviewing process. So far, literature provided no quantitative analysis of this hypothesis. Aims: (1) Quantitatively measure the effects of change decomposition on the outcome of code review (in terms of number of found defects, wrongly reported issues, suggested improvements, time, and understanding); (2) Qualitatively analyze how subjects approach the review and navigate the code, building knowledge and addressing existing issues, in large vs. decomposed changes. Method: Controlled experiment using the pull-based development model involving 28 software developers among professionals and graduate students. Results: Change decomposition leads to fewer wrongly reported issues, influences how subjects approach and conduct the review activity (by increasing context-seeking), yet impacts neither understanding the change rationale nor the number of found defects. Conclusions: Change decomposition reduces the noise for subsequent data analyses but also significantly supports the tasks of the developers in charge of reviewing the changes. As such, commits belonging to different concepts should be separated, adopting this as a best practice in software engineering