The Role of [email protected] in Autonomic Systems:keynote

Autonomic systems manage their own behaviour in accordance with high-level goals. This paper presents a brief outline of challenges related to Autonomic Computing due to uncertainty in the operational environments, and the role that [email protected] play in meeting them. We argue that the existing progress in Autonomic Computing can be further exploited with the support of runtime models. We briefly discuss our ideas related to the need to understand the extent to which the high-level goals of the autonomic system are being satisfied to support decision-making based on runtime evidence and, the need to support self-explanation

Run-time model evaluation for requirements model-driven self-adaptation

A self-adaptive system adjusts its configuration to tolerate changes in its operating environment. To date, requirements modeling methodologies for self-adaptive systems have necessitated analysis of all potential system configurations, and the circumstances under which each is to be adopted. We argue that, by explicitly capturing and modelling uncertainty in the operating environment, and by verifying and analysing this model at runtime, it is possible for a system to adapt to tolerate some conditions that were not fully considered at design time. We showcase in this paper our tools and research results

MODELS 2017 doctoral symposium summary

The Doctoral Symposium at MODELS 2017 brought together nine (9) doctoral students and at least eight (8) mentors who volunteered to spend a day discussing student research presentations. A truly international representation among students and mentors provided a diverse opportunity to offer suggestions and advice regarding the direction and vision of the student PhD ideas. In this document, we summarised the activities and digest the topics discussed during the Symposium

A survey on preferences of quality attributes in the decision-making for self-adaptive systems:The bad, the good and the ugly

Different techniques have been used to specify preferences for quality attributes and decision-making strategies of self-adaptive systems (SAS). These preferences are defined during requirement specification and design time. Further, it is well known that correctly identifying the preferences associated with the quality attributes is a major difficulty. This is exacerbated in the case of SAS, as the preferences defined at design time may not apply to contexts found at runtime. This paper aims at making an exploration of the research landscape that have addressed decision-making and quality attribute preferences specification for selfadaptation, in order to identify new techniques that can improve the current state-of-the-art of decision-making to support self-adaptation. In this paper we (1) review different techniques that support decisionmaking for self-adaptation and identify limitations with respect to the identification of preferences and weights (i.e. the research gap), (2) identify existing solutions that deal with current limitations

Run-time Resolution of Uncertainty

International audienceRequirements awareness should help optimize requirements satisfaction when factors that were uncertain at design time are resolved at runtime. We use the notion of claims to model assumptions that cannot be verified with confidence at design time. By monitoring claims at runtime, their veracity can be tested. If falsified, the effect of claim negation can be propagated to the system's goal model and an alternative means of goal realization selected automatically, allowing the dynamic adaptation of the system to the prevailing environmental contex

Non-human Modelers:Challenges and Roadmap for Reusable Self-explanation

Increasingly, software acts as a “non-human modeler” (NHM), managing a model according to high-level goals rather than a predefined script. To foster adoption, we argue that we should treat these NHMs as members of the development team. In our GrandMDE talk, we discussed the importance of three areas: effective communication (self-explanation and problem-oriented configuration), selection, and process integration. In this extended version of the talk, we will expand on the self-explanation area, describing its background in more depth and outlining a research roadmap based on a basic case study

Tracing Requirements for Adaptive Systems using Claims

International audienceThe complexity of environments faced by dynamically adap- tive systems (DAS) means that the RE process will often be iterative with analysts revisiting the system speci¯cations based on new environmental understanding product of ex- periences with experimental deployments, or even after ¯nal deployments. An ability to trace backwards to an identi¯ed environmental assumption, and to trace forwards to ¯nd the areas of a DAS's speci¯cation that are a®ected by changes in environmental understanding aids in supporting this nec- essarily iterative RE process. This paper demonstrates how claims can be used as markers for areas of uncertainty in a DAS speci¯cation. The paper demonstrates backward tracing using claims to identify faulty environmental under- standing, and forward tracing to allow generation of new behaviour in the form of policy adaptations and models for transitioning the running system

Dynamic decision networks for decision-making in self-adaptive systems: a case study

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and specifically in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, specifically Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision-making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential benefits of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision

Summary of the 9th Workshop on [email protected]

The [email protected] workshop (MRT) series offers a discussion forum for the rising need to leverage modeling techniques at runtime for the software of the future. MRT has become a mature research topic, which is, e.g., reflected in separate sessions at conferences covering MRT approaches only. The target venues of the workshops audience changed from workshops to conferences. Hence, new topics in the area of MRT need to be identified, which are not yet mature enough for conferences. In consequence, the main goal of this edition was to reflect on the past decade of the workshop's history and to identify new future directions for the workshop