The Vision of Autonomic Computing
Computer
QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
Tracking time-varying parameters in software systems with extended Kalman filters
CASCON '05 Proceedings of the 2005 conference of the Centre for Advanced Studies on Collaborative research
QEST '05 Proceedings of the Second International Conference on the Quantitative Evaluation of Systems
QoS management in service-oriented architectures
Performance Evaluation
Adaptive Service Composition in Flexible Processes
IEEE Transactions on Software Engineering
Performance Model Estimation and Tracking Using Optimal Filters
IEEE Transactions on Software Engineering
A framework for QoS-aware binding and re-binding of composite web services
Journal of Systems and Software
ICSOC '07 Proceedings of the 5th international conference on Service-Oriented Computing
Designing Self-Organization for Evolvable Assembly Systems
SASO '08 Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Quality Prediction of Service Compositions through Probabilistic Model Checking
QoSA '08 Proceedings of the 4th International Conference on Quality of Software-Architectures: Models and Architectures
Using quantitative analysis to implement autonomic IT systems
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Model evolution by run-time parameter adaptation
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Optimizing Service Systems Based on Application-Level QoS
IEEE Transactions on Services Computing
Performance prediction of web service workflows
QoSA'07 Proceedings of the Quality of software architectures 3rd international conference on Software architectures, components, and applications
Using observation ageing to improve markovian model learning in QoS engineering
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Run-time efficient probabilistic model checking
Proceedings of the 33rd International Conference on Software Engineering
Dynamic QoS Management and Optimization in Service-Based Systems
IEEE Transactions on Software Engineering
PRISM 4.0: verification of probabilistic real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Ymer: a statistical model checker
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
A formal approach to adaptive software: continuous assurance of non-functional requirements
Formal Aspects of Computing
Assume-Guarantee verification for probabilistic systems
TACAS'10 Proceedings of the 16th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Self-adaptive software needs quantitative verification at runtime
Communications of the ACM
Compositional reverification of probabilistic safety properties for large-scale complex IT systems
Proceedings of the 17th Monterey conference on Large-Scale Complex IT Systems: development, operation and management
An incremental verification framework for component-based software systems
Proceedings of the 16th International ACM Sigsoft symposium on Component-based software engineering
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A growing number of business and safety-critical services are delivered by computer systems designed to reconfigure in response to changes in workloads, requirements and internal state. In recent work, we showed how a formal technique called continual verification can be used to ensure that such systems continue to satisfy their reliability and performance requirements as they evolve, and we presented the challenges associated with the new technique. In this paper, we address important instances of two of these challenges, namely the maintenance of up-to-date reliability models and the adoption of continual verification in engineering practice. To address the first challenge, we introduce a new method for learning the parameters of the reliability models from observations of the system behaviour. This method is capable of adapting to variations in the frequency of the available system observations, yielding faster and more accurate learning than existing solutions. To tackle the second challenge, we present a new software engineering tool that enables developers to use our adaptive learning and continual verification in the area of service-based systems, without a formal verification background and with minimal effort.