Toward a Software Product Line for Affective-Driven Self-Adaptive Systems

Javier Gonzalez-Sanchez

Arizona State University, USA

Track: Doctoral Symposium

One expected characteristic in modern systems is self-adaptation, the capability of monitoring and reacting to changes into the environment. A particular case of self-adaptation is affective-driven self-adaptation. Affective-driven self-adaptation is about having consciousness of users affects (emotions) and drive self-adaptation reacting to changes in those affects. Most of the previous work around self-adaptive systems deals with performance, resources, and error recovery as variables that trigger a system reaction. Moreover, most effort around affect recognition has been put towards offline analysis of affect, and to date only few applications exist that are able to infer users affect in real-time and trigger self-adaptation mechanisms. In response to this deficit, this work proposes a software product line approach to jump-start the development of affect-driven self-adaptive systems by offering the definition of a domain-specific architecture, a set of components (organized as a framework), and guidelines to tailor those components. Study cases with systems for learning and gaming will confirm the capability of the software product line to provide desired functionalities and qualities.