This paper proposes a rule language for defining social expectations based on a metric interval temporal logic with past and future modalities and a current time binding operator. An algorithm for run-time monitoring compliance of rules in this language based on formula progression is also presented.
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As the number of object-oriented software systems increases, it becomes more important for organizations to maintain those systems effectively. However, currently only a small number of maintainability prediction models are available for objectoriented systems. This paper presents a Bayesian network maintainability prediction model for an object-oriented software system. The model is constructed using object-oriented metric data in Li and Henry’s datasets, which were collected from two different object-oriented systems. Prediction accuracy of the model is evaluated and compared with commonly used regression-based models. The results suggest that the Bayesian network model can predict maintainability more accurately than the regression-based models for one system, and almost as accurately as the best regression-based model for the other system.
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Increasing complexity of software applications forces researchers to look for automated ways of programming and adapting these systems. Self-adapting, self-organising software system is one of the possible ways to tackle and manage higher complexity. A set of small independent problem solvers, working together in a dynamic environment, solving multiple tasks, and dynamically adapting to changing requirements is one way of achieving true self-adaptation in software systems. Our work presents a dynamic multi-task environment and experiments with a self-adapting software system. The Evolvable Virtual Machine (EVM) architecture is a model for building complex hierarchically organised software systems. The intrinsic properties of EVM allow the independent programs to evolve into higher levels of complexity, in a way analogous to multi-level, or hierarchical evolutionary processes. The EVM is designed to evolve structures of self-maintaining, self-adapting ensembles, that are open-ended and hierarchically organised. This article discusses the EVM architecture together with different statistical exploration methods that can be used with it. Based on experimental results, certain behaviours that exhibit self-adaptation in the EVM system are discussed.
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Cost is a major obstacle to the adoption of large-scale data integration solutions by small to medium enterprises (SME’s). We therefore propose a lightweight data integration architecture built around the Atom XML syndication format, which may provide a cost-effective alternative technology for SME’s to facilitate data integration, compared to expensive enterprise grade systems. The paper discusses the underlying principles and motivation for the architecture, the structure of the architecture itself, and our research goals.
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Rapid changes in the business environment call for more flexible and adaptive workflow systems. Researchers have proposed that Workflow Management Systems (WfMSs) comprising multiple agents can provide these capabilities. We have developed a multi-agent based workflow system, JBees, which supports distributed process models and the adaptability of executing processes. Modern workflow systems should also have the flexibility to integrate available web services as they are updated. In this paper we discuss how our agent-based architecture can be used to bind and access web services in the context of executing a workflow process model. We use an example from the diamond processing industry to show how our agent architecture can be used to integrate web services with WfMSs.
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In this paper we describe a graphical notation for physical database modelling. This notation provides database administrators with a means to model the physical structure of new and existing databases, thus enabling them to make more proactive and informed tuning decisions, compared to existing database monitoring tools.
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