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Performance prediction and measurement approaches for component-based software systems help software architects to evaluate their systems based on component performance specifications created by component developers. Integrating classical performance models such as queueing networks, stochastic Petri nets, or stochastic process algebras, these approaches additionally exploit benefits of component-based software engineering, such as reuse and division of work. Although researchers have proposed many approaches into this direction during the last decade, none of them has attained widespread industrial use. On this behalf, we have conducted a comprehensive state-of-the-art survey of more than 20 of these approaches assessing their applicability. We classified the approaches according to the expressiveness of their component performance modelling languages. Our survey helps practitioners to select an appropriate approach and scientists to identify interesting topics for future research.

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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Long-living software systems are sustainable if they can be cost-efficiently maintained and evolved over their entire lifecycle. The quality of software architectures determines sustainability to a large extent. Scenario-based software architecture evaluation methods can support sustainability analysis, but they are still reluctantly used in practice. They are also not integrated with architecture-level metrics when evaluating implemented systems, which limits their capabilities. Existing literature reviews for architecture evaluation focus on scenario-based methods, but do not provide a critical reflection of the applicability of such methods for sustainability evaluation. Our goal is to measure the sustainability of a software architecture both during early design using scenarios and during evolution using scenarios and metrics, which is highly relevant in practice. We thus provide a systematic literature review assessing scenario-based methods for sustainability support and categorize more than 40 architecture-level metrics according to several design principles. Our review identifies a need for further empirical research, for the integration of existing methods, and for the more efficient use of formal architectural models.

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