Performance solutions: a practical guide to creating responsive, scalable software
Performance solutions: a practical guide to creating responsive, scalable software
Performance-related completions for software specifications
Proceedings of the 24th International Conference on Software Engineering
Performance modeling from software components
WOSP '04 Proceedings of the 4th international workshop on Software and performance
Model-Based Performance Prediction in Software Development: A Survey
IEEE Transactions on Software Engineering
Compositional Generation of Software Architecture Performance QN Models
WICSA '04 Proceedings of the Fourth Working IEEE/IFIP Conference on Software Architecture
How far are we from the definition of a common software performance ontology?
Proceedings of the 5th international workshop on Software and performance
Automatic Inclusion of Middleware Performance Attributes into Architectural UML Software Models
IEEE Transactions on Software Engineering
Model-Driven Software Development: Technology, Engineering, Management
Model-Driven Software Development: Technology, Engineering, Management
Model-Based performance prediction with the palladio component model
WOSP '07 Proceedings of the 6th international workshop on Software and performance
Performance analysis of security aspects in UML models
WOSP '07 Proceedings of the 6th international workshop on Software and performance
The Future of Software Performance Engineering
FOSE '07 2007 Future of Software Engineering
Integrating performance and reliability analysis in a non-functional MDA framework
FASE'07 Proceedings of the 10th international conference on Fundamental approaches to software engineering
CBSE'06 Proceedings of the 9th international conference on Component-Based Software Engineering
Optimising multiple quality criteria of service-oriented software architectures
Proceedings of the 1st international workshop on Quality of service-oriented software systems
Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering
Parametric performance completions for model-driven performance prediction
Performance Evaluation
What is CIM: an information system perspective
ADBIS'09 Proceedings of the 13th East European conference on Advances in Databases and Information Systems
A metamodelling approach to behavioural modelling
Proceedings of the Fourth Workshop on Behaviour Modelling - Foundations and Applications
Model transformations in non-functional analysis
SFM'12 Proceedings of the 12th international conference on Formal Methods for the Design of Computer, Communication, and Software Systems: formal methods for model-driven engineering
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Model-driven performance prediction methods use abstract design models to predict the performance of the modelled system during early development stages. However, performance is an attribute of the running system and not its model. The system contains many implementation details not part of its model but still affecting the performance at run-time. Existing approaches neglect details of the implementation due to the abstraction underlying the design model. Completion components [26] deal with this problem, however, they have to be added manually to the prediction model. In this work, we assume that the system's implementation is generated by a chain of model transformations. In this scenario, the transformation rules determine the transformation result. By analysing these transformation rules, a second transformation can be derived which automatically adds details to the prediction model according to the encoded rules. We call this transformation a coupled transformation as it is coupled to an corresponding model-to-code transformation. It uses the knowledge on the output of the model-to-code transformation to increase performance prediction accuracy. The introduced coupled transformations method is validated in a case study in which a parametrised transformation maps abstract component connectors to realisations in different RPC calls. In this study, the corresponding coupled transformation captures the RPC's details with a prediction error of less than 5%.