Performance queries for architecture-level performance models

Over the past few decades, many performance modeling formalisms and prediction techniques for software architectures have been developed in the performance engineering community. However, using a performance model to predict the performance of a software system normally requires extensive experience with the respective modeling formalism and involves a number of complex and time consuming manual steps. In this paper, we propose a generic declarative interface to performance prediction techniques to simplify and automate the process of using architecture-level software performance models for performance analysis. The proposed Descartes Query Language (DQL) is a language to express the demanded performance metrics for prediction as well as the goals and constraints of the specific prediction scenario. It reduces the manual effort and learning curve in working with performance models by a unified interface independent of the employed modeling formalism. We evaluate the applicability and benefits of the proposed approach in the context of several representative case studies.