Abstract. The goal of this roadmap paper is to summarize the state-ofthe-art and to identify critical challenges for the systematic software engineering of self-adaptive systems. The paper is partitioned into four parts, one for each of the identified essential views of self-adaptation: modelling dimensions, requirements, engineering, and assurances. For each view, we present the state-of-the-art and the challenges that our community must address. This roadmap paper is a result of the Dagstuhl Seminar 08031 on "Software Engineering for Self-Adaptive Systems, " which took place in January 2008.

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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Abstract. Self-adaptation is typically realized using a control loop. One prominent approach for organizing a control loop in self-adaptive systems is by means of four components that are responsible for the primary functions of self-adaptation: Monitor, Analyze, Plan, and Execute, together forming a MAPE loop. When systems are large, complex, and heterogeneous, a single MAPE loop may not be sufficient for managing all adaptation in a system, so multiple MAPE loops may be introduced. In self-adaptive systems with multiple MAPE loops, decisions about how to decentralize each of the MAPE functions must be made. These decisions involve how and whether the corresponding functions from multiple loops are to be coordinated (e.g., planning components coordinating to prepare a plan for an adaptation). To foster comprehension of self-adaptive systems with multiple MAPE loops and support reuse of known solutions, it is crucial that we document common design approaches for engineers. As such systematic knowledge is currently lacking, it is timely to reflect on these systems to: (a) consolidate the knowledge in this area, and (b) to develop a systematic approach for describing different types of control in self-adaptive systems. We contribute with a simple notation for describing interacting MAPE loops, which we believe helps in achieving (b), and we use this notation to describe a number of existing patterns of interacting MAPE loops, to begin to fulfill (a). From our study, we outline numerous remaining research challenges in this area.

Models can help software engineers to reason about design-time decisions before implementing a system. This paper focuses on models that deal with non-functional properties, such as reliability and performance. To build such models, one must rely on numerical estimates of various parameters provided by domain experts or extracted by other similar systems. Unfortunately, estimates are seldom correct. In addition, in dynamic environments, the value of parameters may change over time. We discuss an approach that addresses these issues by keeping models alive at run time and feeding a Bayesian estimator with data collected from the running system, which produces updated parameters. The updated model provides an increasingly better representation of the system. By analyzing the updated model at run time, it is possible to detect or predict if a desired property is, or will be, violated by the running implementation. Requirement violations may trigger automatic reconfigurations or recovery actions aimed at guaranteeing the desired goals. We illustrate a working framework supporting our methodology and apply it to an example in which a Web service orchestrated composition is modeled through a discrete time Markov chain. Numerical simulations show the effectiveness of the approach

Continually verify self-adaptation decisions taken by critical software in response to changes in the operating environment.

Abstract-Architecting software systems according to the serviceoriented paradigm, and designing runtime self-adaptable systems are two relevant research areas in today's software engineering. In this paper we address issues that lie at the intersection of these two important fields. First, we present a characterization of the problem space of self-adaptation for service-oriented systems, thus providing a frame of reference where our and other approaches can be classified. Then, we present MOSES, a methodology and a software tool implementing it to support QoS-driven adaptation of a service-oriented system. It works in a specific region of the identified problem space, corresponding to the scenario where a service-oriented system architected as a composite service needs to sustain a traffic of requests generated by several users. MOSES integrates within a unified framework different adaptation mechanisms. In this way it achieves a greater flexibility in facing various operating environments and the possibly conflicting QoS requirements of several concurrent users. Experimental results obtained with a prototype implementation of MOSES show the effectiveness of the proposed approach.Index Terms-Service-oriented architecture, runtime adaptation, quality of service.

Runtime adaptation is recognized as a viable way for a serviceoriented system to meet QoS requirements in its volatile operating environment. In this paper we propose a methodology to drive the adaptation of such a system, that integrates within a unified framework different adaptation mechanisms, to achieve a greater flexibility in facing different operating environments and the possibly conflicting QoS requirements of several concurrent users. To determine the most suitable adaptation action(s), the methodology is based on the formulation and solution of a linear programming problem, which is derived from a behavioral model of the system updated at runtime by a monitoring activity. Numerical experiments show the effectiveness of our approach. Besides the methodology, we also present a prototype tool that implements it.