ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
Predicting performance via automated feature-interaction detection
Proceedings of the 34th International Conference on Software Engineering
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
Non-functional requirements in model-driven software product line engineering
Proceedings of the Fourth International Workshop on Nonfunctional System Properties in Domain Specific Modeling Languages
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We propose to integrate performance analysis in the early phases of the model-driven development process for Software Product Lines (SPL). We start with a multi-view UML model of the core family assets representing the commonality and variability between different products, which we call the SPL model. We add another perspective to the SPL model, annotating it with generic performance specifications expressed in the standard UML profile MARTE, recently adopted by OMG. The runtime performance of a product is affected by factors contained in the UML model of the product (derived from the SPL model), but also by external factors depending on the implementation and execution environments. The external factors not contained in the SPL model need to be eventually represented in the performance model. In order to do so, we propose to represent the variability space of different possible implementation and execution environments through a so called "performance completion (PC) feature model". These PC features are mapped to MARTE performance-related stereotypes and attributes attached to the SPL model elements. A first model transformation realized in the Atlas Transformation Language (ATL) derives the UML model of a specific product with concrete MARTE annotations from the SPL model. A second transformation generates a Layered Queueing Network (LQN) performance model for the given product by applying an existing transformation named PUMA, developed in previous work. The proposed technique is illustrated with an e-commerce case study. A LQN model is derived for a product and the impact of different levels of secure communication channels on its performance is analyzed by using the LQN model.