Parametric performance completions for model-driven performance prediction

Performance prediction methods can help software architects to identify potential performance problems, such as bottlenecks, in their software systems during the design phase. In such early stages of the software life-cycle, only a little information is available about the system's implementation and execution environment. However, these details are crucial for accurate performance predictions. Performance completions close the gap between available high-level models and required low-level details. Using model-driven technologies, transformations can include details of the implementation and execution environment into abstract performance models. However, existing approaches do not consider the relation of actual implementations and performance models used for prediction. Furthermore, they neglect the broad variety of possible implementations and middleware platforms, possible configurations, and possible usage scenarios. In this paper, we (i) establish a formal relation between generated performance models and generated code, (ii) introduce a design and application process for parametric performance completions, and (iii) develop a parametric performance completion for Message-oriented Middleware according to our method. Parametric performance completions are independent of a specific platform, reflect performance-relevant software configurations, and capture the influence of different usage scenarios. To evaluate the prediction accuracy of the completion for Message-oriented Middleware, we conducted a real-world case study with the SPECjms2007 Benchmark [http://www.spec.org/jms2007/]. The observed deviation of measurements and predictions was below 10% to 15%.