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.

This paper presents some initial results from a twelve-month empirical research study of model driven engineering (MDE). Using largely qualitative questionnaire and interview methods we investigate and document a range of technical, organizational and social factors that apparently influence organizational responses to MDE: specifically, its perception as a successful or unsuccessful organizational intervention. We then outline a range of lessons learned. Whilst, as with all qualitative research, these lessons should be interpreted with care, they should also be seen as providing a greater understanding of MDE practice in industry, as well as shedding light on the varied, and occasionally surprising, social, technical and organizational factors that affect success and failure. We conclude by suggesting how the next phase of the research will attempt to investigate some of these issues from a different angle and in greater depth.

Despite lively debate over the last decade on the benefits or drawbacks of model-driven engineering (MDE), there have been very few industry-wide studies of MDE in practice. We present a new study, covering a broad range of experiences and ways of applying MDE: we surveyed 450 MDE practitioners and carried out in-depth interviews with 22 more. Findings suggest that MDE may be more widespread than commonly believed, but developers rarely use it to generate whole systems; rather, they apply it to develop key parts of a system often using domain-specific modeling languages developed specifically for the purpose. Our findings also suggest reasons why some efforts to adopt MDE fail and some succeed. As is usually the case in software engineering, adoption largely depends on social and organizational factors, some of which we describe in this paper.

Self-adaptive systems have the capability to autonomously modify their behaviour at run-time in response to changes in their environment. Self-adaptation is particularly necessary for applications that must run continuously, even under adverse conditions and changing requirements; sample domains include automotive systems, telecommunications, and environmental monitoring systems. While a few techniques have been developed to support the monitoring and analysis of requirements for adaptive systems, limited attention has been paid to the actual creation and specification of requirements of self-adaptive systems. As a result, self-adaptivity is often constructed in an ad-hoc manner. In this paper, we argue that a more rigorous treatment of requirements explicitly relating to self-adaptivity is needed and that, in particular, requirements languages for self-adaptive systems should include explicit constructs for specifying and dealing with the uncertainty inherent in self-adaptive systems. We present RELAX, a new requirements language for selfadaptive systems and illustrate it using examples from the smart home domain.

In this paper, we attempt to address the relative absence of empirical studies of model driven engineering through describing the practices of three commercial organizations as they adopted a model driven engineering approach to their software development. Using in-depth semi-structured interviewing we invited practitioners to reflect on their experiences and selected three to use as exemplars or case studies. In documenting some details of attempts to deploy model driven practices, we identify some 'lessons learned', in particular the importance of complex organizational, managerial and social factors -as opposed to simple technical factors -in the relative success, or failure, of the endeavour. As an example of organizational change management the successful deployment of model driven engineering appears to require: a progressive and iterative approach; transparent organizational commitment and motivation; integration with existing organizational processes and a clear business focus.

Abstract-Requirements are sensitive to the context in which the system-to-be must operate. Where such context is well-understood and is static or evolves slowly, existing RE techniques can be made to work well. Increasingly, however, development projects are being challenged to build systems to operate in contexts that are volatile over short periods in ways that are imperfectly understood. Such systems need to be able to adapt to new environmental contexts dynamically, but the contextual uncertainty that demands this self-adaptive ability makes it hard to formulate, validate and manage their requirements. Different contexts may demand different requirements trade-offs. Unanticipated contexts may even lead to entirely new requirements. To help counter this uncertainty, we argue that requirements for selfadaptive systems should be run-time entities that can be reasoned over in order to understand the extent to which they are being satisfied and to support adaptation decisions that can take advantage of the systems' self-adaptive machinery. We take our inspiration from the fact that explicit, abstract representations of software architectures used to be considered design-time-only entities but computational reflection showed that architectural concerns could be represented at run-time too, helping systems to dynamically reconfigure themselves according to changing context. We propose to use analogous mechanisms to achieve requirements reflection. In this paper we discuss the ideas that support requirements reflection as a means to articulate some of the outstanding research challenges.

No abstract

Self-adaptive systems have the capability to autonomously modify their behavior at run-time in response to changes in their environment. Self-adaptation is particularly necessary for applications that must run continuously, even under adverse conditions and changing requirements; sample domains include automotive systems, telecommunications, and environmental monitoring systems. While a few techniques have been developed to support the monitoring and analysis of requirements for adaptive systems, limited attention has been paid to the actual creation and specification of requirements of self-adaptive systems. As a result, self-adaptivity is often constructed in an ad-hoc manner. In order to support the rigorous specification of adaptive systems requirements, this paper introduces RELAX, a new requirements language for selfadaptive systems that explicitly addresses uncertainty inherent in adaptive systems. We present the formal semantics for RELAX in terms of fuzzy logic, thus enabling a rigorous treatment of requirements that include uncertainty. RELAX enables developers to identify uncertainty in the requirements, thereby facilitating the design of systems that are, by definition, more flexible and amenable to adaptation in a systematic fashion. We illustrate the use of RELAX on smart home applications, including an adaptive assisted living system. IntroductionAs applications continue to grow in size, complexity, and heterogeneity, it becomes increasingly necessary for computing-based systems to dynamically self-adapt to changing environmental conditions. We call these systems dynamically adaptive systems (DASs). Example applications that require DAS capabilities include automotive systems, telecommunication systems, environmental monitoring, and power grid management systems. The distributed nature of DASs and changing environmental factors (including human interaction) make it difficult to anticipate all the explicit states in which the system will be during its lifetime. As such, a DAS needs to be able to tolerate a range of environmental conditions and contexts, but the exact nature of these contexts remains imperfectly understood. One overarching challenge in developing DASs, Address(es) of author(s) should be given