The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-a properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.

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Self-adaptive software: Landscape and research challenges

The Theory of Stochastic Processes. By D. R. Cox and H. D. Miller. Pp. x, 398. 70s. (Methuen)

4. Modelling Survival Data in Medical Research

(1991). Soft Systems Methodology in Action. European Journal of Information Systems: Vol. 1, No. 3, pp. 215-216.

https://link.springer.com/content/pdf/10.1057%2Fejis.1991.37.pdf

Soft Systems Methodology in Action

Nature-inspired intelligent swarm technologies deals with complex problems that might be impossible to solve using traditional technologies and approaches. Swarm intelligence techniques (note the difference from intelligent swarms) are population-based stochastic methods used in combinatorial optimization problems in which the collective behavior of relatively simple individuals arises from their local interactions with their environment to produce functional global patterns. Swarm intelligence represents a meta heuristic approach to solving a variety of problems

/pdf/swarms-and-swarm-intelligence-4tukyxvgnk.pdf

Swarms and Swarm Intelligence

https://www.cse.msu.edu/~chengb/CSE891/Handouts/autonomic-computing-concise-intro-Sterritt.pdf

A concise introduction to autonomic computing

Dynamic sensor data segmentation for real-time knowledge-driven activity recognition

Efforts since 2001 to design self-managing systems have yielded many impressive achievements, yet the original vision of autonomic computing remains unfulfilled. Researchers must develop a comprehensive systems engineering approach to create effective solutions for next-generation enterprise and sensor systems.

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Fulfilling the Vision of Autonomic Computing

Autonomic computing is emerging as a significant new approach to the design of computing systems. Its goal is the development of systems that are self-configuring, self-healing, self-protecting and self-optimizing. Dependability is a long-standing desirable property of all computer-based systems. The purpose of the paper is to consider how autonomic computing can provide a framework for dependability.

/pdf/autonomic-computing-a-means-of-achieving-dependability-3l54puknj0.pdf

Roy Sterritt

Papers

Swarms and Swarm Intelligence

A concise introduction to autonomic computing

Dynamic sensor data segmentation for real-time knowledge-driven activity recognition

Fulfilling the Vision of Autonomic Computing

Autonomic Computing - a means of achieving dependability?