Abstract
The evaluation and validation of self-aware computing systems is still a challenging task. Traceability of the behavior makes sure that a running system is behaving as defined at design time, which is important for developers as well as system administrators. In this work, we coin the term “enhanced traceability” for self-aware computing systems based on explainable artificial intelligence, data visualization, and human-computer interaction research. We present EnTrace, a reusable and open source platform that provides enhanced traceability capabilities for self-aware computing systems. EnTrace visualizes the current state of a self-aware computing system and enables users to comprehend the influence of adaptations on functional constraints and non-functional goals. We evaluate EnTrace qualitatively and quantitatively to show its features, its support for distributed and decentralized target systems, as well as its responsiveness.
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