diff --git a/Website/dp2005-abstracts-contents.htm b/Website/dp2005-abstracts-contents.htm index bbf030e..0749212 100644 --- a/Website/dp2005-abstracts-contents.htm +++ b/Website/dp2005-abstracts-contents.htm @@ -8,16 +8,16 @@

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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2005/02: An application of Bayesian network for predicting object-oriented software maintainability

C. van Koten and A. Gray

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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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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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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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