Abstract. The goal of this roadmap paper is to summarize the stateof-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

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Continuous evolution is a key trait of software-intensive systems. Many research projects investigate mechanisms to adapt software systems effectively in order to ease evolution. By observing its internal state and surrounding context continuously using feedback loops, an adaptive system is able to analyze its effectiveness by evaluating quality criteria and then self-tune to improve its operations. The goals of these feedback loops range from keeping single variables in a prescribed range to satisfying non-functional requirements by regulating decentralized, interdependent subsystems.To be able to observe and possibly orchestrate continuous evolution of software systems in a complex and changing environment, we need to push monitoring of evolving systems to unprecedented levels. It has been established that security has to be built into a system from the ground up and cannot be added as an afterthought-the same is probably true for intensive monitoring. We propose to monitor adaptive systems with autonomic elements to enhance their assessment capabilities. In this paper, we discuss how to build monitoring into Java programs from the ground up with reflection technology to detect normal and exceptional system behavior.

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Raven is a Multi-Object Adaptive Optics (MOAO) scientific demonstrator which will be used on-sky at the Subaru observatory. Raven is currently being built at the University of Victoria AO Lab. In this paper, we present an overview of the final Raven design and then describe lab tests involving prototypes of Raven subsystems. The final design includes three open loop wavefront sensors (WFSs), a laser guide star WFS and two figure/truth WFSs. Two science channels, each containing a deformable mirror (DM), feed light to the Subaru IRCS spectrograph. Central to the Raven MOAO system is a Calibration Unit (CU) which contains multiple sources, a telescope simulator including two rotating phase screens and a ground layer DM that can be used to calibrate and test Raven. We are working with the Raven CU and open loop WFSs to test and validate our open loop calibration and alignment techniques.