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Quantitative prediction of quality properties (i.e. extrafunctional properties such as performance, reliability, and cost) of software architectures during design supports a systematic software engineering approach. Designing architectures that exhibit a good trade-off between multiple quality criteria is hard, because even after a functional design has been created, many remaining degrees of freedom in the software architecture span a large, discontinuous design space. In current practice, software architects try to find solutions manually, which is time-consuming, can be errorprone and can lead to suboptimal designs. We propose an automated approach to search the design space for good solutions. Starting with a given initial architectural model, the approach iteratively modifies and evaluates architectural models. Our approach applies a multi-criteria genetic algorithm to software architectures modelled with the Palladio Component Model. It supports quantitative performance, reliability, and cost prediction and can be extended to other quantitative quality criteria of software architectures. We validate the applicability of our approach by applying it to an architecture model of a component-based business information system and analyse its quality criteria trade-offs by automatically investigating more than 1200 alternative design candidates.

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Performance prediction of component-based systems a survey from an engineering perspective.

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In cloud computing, software architects develop systems for virtually unlimited resources that cloud providers account on a pay-per-use basis. Elasticity management systems provision these resources autonomously to deal with changing workload. Such changing workloads call for new objective metrics allowing architects to quantify quality properties like scalability, elasticity, and efficiency, e.g., for requirements/SLO engineering and software design analysis. In literature, initial metrics for these properties have been proposed. However, current metrics lack a systematic derivation and assume knowledge of implementation details like resource handling. Therefore, these metrics are inapplicable where such knowledge is unavailable.To cope with these lacks, this short paper derives metrics for scalability, elasticity, and efficiency properties of cloud computing systems using the goal question metric (GQM) method. Our derivation uses a running example that outlines characteristics of cloud computing systems. Eventually, this example allows us to set up a systematic GQM plan and to derive an initial set of six new metrics. We particularly show that our GQM plan allows to classify existing metrics.

Abstract-During the last decade, researchers have proposed a number of model transformations enabling performance predictions. These transformations map performance-annotated software architecture models into stochastic models solved by means of analytical or numerical analysis or by system simulation. However, so far, a detailed quantitative evaluation of the accuracy and efficiency of different transformations is missing, making it hard to select an adequate transformation for a given context. This paper provides an in-depth comparison and quantitative evaluation of representative model transformations to, e.g., Queueing Petri Nets and Layered Queueing Networks. The semantic gaps between typical source model abstractions and the different analysis techniques are revealed. The accuracy and efficiency of each transformation are evaluated by considering four case studies representing systems of different size and complexity. The presented results and insights gained from the evaluation help software architects and performance engineers to select the appropriate transformation for a given context, thus significantly improving the usability of model transformations for performance prediction.