Designing and Evaluating Context-Aware (Recommender) Systems in Software Engineering.
Context Aware Systems systematically observe their users and their environments to detect the context and automatically adjust and optimize their behavior. Context might include physical data (e.g. location, infrastructure, climate), interaction data (e.g. interaction data, the user intention, user preferences, tools used, information needed, problem encountered), or social data (e.g. comments, the role in the group, and social interactions with the community). Often, these system learn from historical context data to predict current and future context.
In this talk, I will introduce and discuss a particular kind of context aware adaptive systems that focus on supporting software engineering work, e.g. by recommending useful information to developers, managers, and users. Thereby, an important question is how can we reliably collect data to design and evaluate these systems. This question has been intensively discussed in the field of Empirical Software Engineering in the last decade.