Abstract: The Rainbow framework uses software architectures and a reusable infrastructure to support self-adaptation of software systems. The use of external adaptation mechanisms allows the explicit specification of adaptation strategies for multiple system concerns.

Abstract: Explainability refers to the degree to which a software system's actions or solutions can be understood by humans. Giving humans the right amount of explanation at the right time is an important factor in maximizing the effective collaboration between an adaptive system and humans during interaction. However, explanations come with costs, such as the required time of explanation and humans' response time. Hence it is not always clear whether explanations will improve overall system utility and, if so, how the system should effectively provide explanation to humans, particularly given that different humans may benefit from different amounts and frequency of explanation. To provide a partial basis for making such decisions, this paper defines a formal framework that incorporates human personality traits as one of the important elements in guiding automated decision-making about the proper amount of explanation that should be given to the human to improve the overall system utility. Specifically, we use probabilistic model analysis to determine how to utilize explanations in an effective way. To illustrate our approach, Grid - a virtual human and system interaction game -- is developed to represent scenarios for human-systems collaboration and to demonstrate how a human's personality traits can be used as a factor to consider for systems in providing appropriate explanations.

Abstract: Two of the main paradigms used to build adaptive software employ different types of properties to capture relevant aspects of the system’s run-time behavior. On the one hand, control systems consider properties that concern static aspects like stability, as well as dynamic properties that capture the transient evolution of variables such as settling time. On the other hand, self-adaptive systems consider mostly non-functional properties that capture concerns such as performance, reliability, and cost. In general, it is not easy to reconcile these two types of properties or identify under which conditions they constitute a good fit to provide run-time guarantees. There is a need of identifying the key properties in the areas of control and self-adaptation, as well as of characterizing and mapping them to better understand how they relate and possibly complement each other. In this paper, we take a first step to tackle this problem by: (1) identifying a set of key properties in control theory, (2) illustrating the formalization of some of these properties employing temporal logic languages commonly used to engineer self-adaptive software systems, and (3) illustrating how to map key properties that characterize self-adaptive software systems into control properties, leveraging their formalization in temporal logics. We illustrate the different steps of the mapping on an exemplar case in the cloud computing domain and conclude with identifying open challenges in the area.

Abstract: A common approach to adding self-management capabilities to a system is to provide one or more external control modules, whose responsibility is to monitor system behavior, and adapt the system at run time to achieve various goals (configure the system, improve performance, recover from faults, etc.). An important problem arises when there is more than one such self-management module: how can one make sure that they are composed to provide consistent and complementary benefits? In this paper we describe a solution that introduces a self-management coordination architecture and infrastructure to support such composition. We focus on the problem of coordinating self-configuring and self-healing capabilities, particularly with respect to global configuration and incremental repair. We illustrate the approach in the context of a self-managing video teleconference system that composes two pre-existing adaptation modules to achieve synergistic benefits of both.

Abstract: This paper is an experience report on a first attempt to develop and apply a new form of software: a fullservice testbed designed to evaluate alternative software dependability technologies, and to accelerate their maturation and transition into project use. The SCRover testbed includes not only the specifications, code, and hardware of a public safety robot, but also the package of instrumentation, scenario drivers, seeded defects, experimentation guidelines, and comparative effort and defect data needed to facilitate technology evaluation experiments. The SCRover testbed’s initial operational capability has been recently applied to evaluate two architecture definition languages (ADLs) and toolsets, Mae and AcmeStudio. The testbed evaluation showed (1) that the ADL-based toolsets were complementary and costeffective to apply to mission-critical systems; (2) that the testbed was cost-effective to use by researchers; and (3) that collaboration in testbed use by researchers and the Jet Propulsion Laboratory (JPL) project users resulted in actions to accelerate technology maturity and transition into project use. The evaluation also identified a number of lessons learned for improving the SCRover testbed, and for development and application of future technology evaluation testbeds.