Understanding player preference and behavioral tendency in the presence of collaborative opportunities is fundamental to building cooperative video games.1 Such knowledge allows the integration and fine-tuning of features to further engage players. This paper presents an ongoing study that approaches the subject by posing a simple question: when opportunities are provided, would players choose to cooperate? The work analyzes existing well-established cooperative game design patterns and identifies effective attributes of game mechanics that are characterized by the patterns. Small multiplayer games that focused on each of the attributes are built where in each case the players have the options of collaborating or completing the tasks individually. In this way, the effects of each attribute can be analyzed independently to provide insights into players' behavior under specific variations of conditions. While the testing is ongoing, our results will provide directions for future integration of collaborative features in games. Additionally, our approach of identifying implementable attributes based on the principles of cooperative game design patterns serves as a template for systematic approach to building cooperative video games.

The paper presents iTraceVis, an eye tracking visualization component for iTrace, a gaze-aware Eclipse plugin. The visualization component is designed to work with data generated from iTrace after an eye tracking session. iTrace provides us with an automatic mapping of raw eye gaze on corresponding source code elements according to their hierarchy in the abstract syntax graph even in the presence of scrolling and context switching between files. This feature provides us with the ability to visualize eye tracking data in large source code files and is not just restricted to visualize only a method at a time i.e., something that fits on the screen. Due to the enormous size and richness of data collected from the eye tracker, visualizations help both the researcher and the developer to comprehend what transpired during an eye tracking session. iTraceVis currently supports four visualization views - heat map, gaze skyline, static gaze map, and dynamic gaze map. In order to determine the usefulness of the visualizations, we conduct a pilot user study with 10 senior students. We present an existing eye-tracking developer session to the study participants and ask them a series of questions while they interacted with iTraceVis and its various views in Eclipse. The results indicate that the visualizations do indeed help users understand the visualized session represented by the data, and also provide insight into where the visualizations can be improved as part of future work.

A new research paradigm is proposed that leverages developer eye gaze to improve the state of the art in software engineering research and practice. The vision of this new paradigm for use on software engineering tasks such as code summarization, code recommendations, prediction, and continuous traceability is described. Based on this new paradigm, it is foreseen that new benchmarks will emerge based on developer gaze. The research borrows from cognitive psychology, artificial intelligence, information retrieval, and data mining. It is hypothesized that new algorithms will be discovered that work with eye gaze data to help improve current IDEs, thus improving developer productivity. Conducting empirical studies using an eye tracker will lead to inventing, evaluating, and applying innovative methods and tools that use eye gaze to support the developer. The implications and challenges of this paradigm for future software engineering research is discussed.