Extending Context Awareness by Anticipating Uncertainty with Enki and Darjeeling

A self-adaptive system (SAS) requires automated planning that alters its behavior to properly operate in dynamic environments. To select a successful adaptation, the SAS must be context aware, which includes knowledge about a system's internal and environmental conditions, strategies to monitor conditions, and the capability to reason over an adaptation's relevance to its current conditions. Operational and environmental conditions are subject to foreseeable sources of uncertainty. Processes should be embedded in the SAS that generate data across a diverse set of conditions to investigate such sources and anticipate their conditions. Enki is a technology that applies a genetic algorithm to generate scenarios with diverse conditions. These scenarios should be further investigated to configure adaptations that address unexpected system behavior and failures. Darjeeling, an automated program repair tool can accept generated scenarios as input and apply genetic programming to generate patches from failed tests. Our prior work created a framework to evaluate patches by assessing their risk of requirements violation and their degree of security compliance confidence. In this paper, we incorporate these third-party tools, Enki and Darjeeling, into our framework that employs a MAPE-K loop of a previous assessed example system to extend its context awareness and increases automated capabilities.