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Requirements-aware Systems for Self-adaptation under Uncertainty Research Statement
"... The development of software-intensive systems is driven by their requirements. Traditional requirements engineering (RE) methods focus on resolving ambiguities in requirements and advocate specifying require-ments in sufficient detail so that the implementation can be checked against them for confor ..."
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The development of software-intensive systems is driven by their requirements. Traditional requirements engineering (RE) methods focus on resolving ambiguities in requirements and advocate specifying require-ments in sufficient detail so that the implementation can be checked against them for conformance. In an ideal world, this way of thinking can be very effective. Requirements can be specified clearly, updated as necessary, and evolutions of the software design can be made with the requirements in mind. Increasingly, however, it is not sufficient to fix requirements statically because they will change at runtime as the operating environment changes. Furthermore, as software systems become more pervasive, there is growing uncertainty about the environment and so requirements changes cannot be predicted at design-time [12, 24, 39, 1, 20]. It is considerations such as these that have led to the development of self-adaptive systems (SASs) [11], which have the ability to dynamically and autonomously reconfigure their behavior to respond to changing external conditions. Consider a scenario involving a robot vacuum cleaner for domestic apartments. The vacuum cleaner has goals clean apartment, avoid tripping hazard and minimize energy costs. Further, it has the domain assumption energy is cheapest at night. To satisfy the avoid tripping hazard goal, a requirement is derived that it should stop operating as soon as any human activity is detected. Night operation satisfies the
Uncertainty Handling in Goal-Driven Self-Optimization – Limiting the Negative Effect on Adaptation
"... and other research outputs Uncertainty handling in goal-driven self-optimization – limiting the negative effect on adaptation ..."
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and other research outputs Uncertainty handling in goal-driven self-optimization – limiting the negative effect on adaptation
in Supporting On-the-fly
"... Abstract Models at runtime can be defined as abstract representations of a system, including its structure and behaviour, which exist in tandem with the given system during the actual execution time of that system. Furthermore, these models should be causally connected to the system being modelled, ..."
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Abstract Models at runtime can be defined as abstract representations of a system, including its structure and behaviour, which exist in tandem with the given system during the actual execution time of that system. Furthermore, these models should be causally connected to the system being modelled, offer-ing a reflective capability. Significant advances have been made in recent years in applying this concept, most notably in adaptive systems. In this paper we argue that a similar approach can also be used to support the dynamic gen-eration of software artefacts at execution time. An important area where this is relevant is the generation of software mediators to tackle the crucial prob-lem of interoperability in distributed systems. We refer to this approach as emergent middleware, representing a fundamentally new approach to resolv-ing interoperability problems in the complex distributed systems of today. In this context, the runtime models are used to capture meta-information about the underlying networked systems that need to interoperate, including their interfaces and additional knowledge about their associated behaviour. This is supplemented by ontological information to enable semantic reasoning. This paper focuses on this novel use of models at runtime, examining in detail the nature of such runtime models coupled with consideration of the supportive algorithms and tools that extract this knowledge and use it to synthesise the appropriate emergent middleware.
Stateful Requirements Monitoring for Self-Repairing Socio-Technical Systems
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