One important characteristic in modern software systems is self-
adaptation, the capability of monitoring and reacting to changes into the environment. A particular case of self-adaptation is affect-driven self- adaptation. Affect-driven self-adaptation involves using sensing devices to measure physiological signals of human affectivestate’s (emotions) changes, learning about the meaning of those changes, and then reacting (self-adapting) in consequence. Affect-driven self-adaptive systems take advantage of brain-computer interfaces, eye-tracking, face-based emotion recognition, and sensors to measure physiological signals.
Systems such as learning environments, health care systems, and videogames are able to take advantage of affect-driven self-adaptive capabilities. Today these capabilities are brittle, costly to change, difficult to reuse, and limited in scope. A software factory approach has been suggested to make feasible adding affective-driven self-adaptive capabilities either into new or existing systems. Software factories capture knowledge of how to produce applications that share common characteristics and make that knowledge available in the form of assets (patterns, model, framework, and tools) and systematically apply those assets to automate development reducing cost and time while improving product quality.
This talk provides a sneak peek of affective-driven self-adaptive capabilities and explores how a software factory approach is used to build affect-driven self-adaptive software.