Exploiting Contextual and Social Variability for Software Adaptation

Self-adaptive software systems are systems that monitor their environment and compensate if there are deviations from their requirements. Self-adaptivity is gaining prominence as an approach to lowering software costs by reducing the need for manual system maintenance. Self-adaptivity is particularly important for distributed systems that involve both software and human/organizational actors because of the volatility as well as uncertainty that permeates their operational environments. We refer to such systems as Socio-Technical System (STS). The thesis proposes a comprehensive framework for designing self-adaptive software that operates within a socio-technical system. The framework is founded upon the notions of contextual and social variability. A key ingredient of our approach is to rely on high-level abstractions to represent the purpose of the system (requirements model), to explicitly represent the commitments that exist among participating actors in an STS, and also to consider how operational context influences requirements. The proposed framework consists of (i) modelling and analysis techniques for representing and reasoning about contextual and social variability; (ii) a conceptual architecture for self-adaptive STSs; and (iii) a set of algorithms to diagnose a failure and to compute and select a new variant that addresses the failure. To evaluate our proposal, we developed two prototype implementations of our architecture to demonstrate different features of our framework, and successfully applied them to two case studies. In addition, the thesis reports encouraging results on experiments we conducted with our implementations in order to check for scalability.