The Evolving Philosophers Problem: Dynamic Change Management
IEEE Transactions on Software Engineering
Discovering models of software processes from event-based data
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Computer
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IEEE Transactions on Software Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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Introduction to Data Mining, (First Edition)
An introduction to ROC analysis
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FOSE '07 2007 Future of Software Engineering
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IEEE Transactions on Software Engineering
A novel approach for process mining based on event types
Journal of Intelligent Information Systems
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Software Engineering for Self-Adaptive Systems
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The VLDB Journal — The International Journal on Very Large Data Bases
Version-consistent dynamic reconfiguration of component-based distributed systems
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Utilizing architectural styles to enhance the adaptation support of middleware platforms
Information and Software Technology
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An important challenge in dynamic adaptation of a software system is to prevent inconsistencies (failures) and disruptions in its operations during and after change. Several prior techniques have solved this problem with various tradeoffs. All of them, however, assume the availability of detailed component dependency models. This paper presents a complementary technique that solves this problem in settings where such models are either not available, difficult to build, or outdated due to the evolution of the software. Our approach first mines the execution history of a software system to infer a stochastic component dependency model, representing the probabilistic sequence of interactions among the system's components. We then demonstrate how this model could be used at runtime to infer the "best time" for adaptation of the system's components. We have thoroughly evaluated this research on a multi-user real world software system and under varying conditions.