[서평]「Component Software」 - Beyond Object-Oriented Programming -

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

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Handbook of software reliability engineering

The gap between predicted and measured energy performance of buildings: A framework for investigation

The Palladio component model for model-driven performance prediction

This book gathers chapters from some of the top international empirical software engineering researchers focusing on the practical knowledge necessary for conducting, reporting and using empirical methods in software engineering. Topics and features include guidance on how to design, conduct and report empirical studies. The volume also provides information across a range of techniques, methods and qualitative and quantitative issues to help build a toolkit applicable to the diverse software development contexts

/pdf/guide-to-advanced-empirical-software-engineering-2f0my2wuh8.pdf

Guide to Advanced Empirical Software Engineering

http://www.koziolek.de/docs/Koziolek2010-PE-preprint.pdf

Performance evaluation of component-based software systems: A survey

One aim of component-based software engineering (CBSE) is to enable the prediction of extra-functional properties, such as performance and reliability, utilising a well-defined composition theory. Nowadays, such theories and their accompanying prediction methods are still in a maturation stage. Several factors influencing extra-functional properties need additional research to be understood. A special problem in CBSE stems from its specific development process: Software components should be specified and implemented independent from their later context to enable reuse. Thus, extra-functional properties of components need to be specified in a parametric way to take different influence factors like the hardware platform or the usage profile into account. In our approach, we use the Palladio Component Model (PCM) to specify component-based software architectures in a parametric way. This model offers direct support of the CBSE development process by dividing the model creation among the developer roles. In this paper, we present our model and a simulation tool based on it, which is capable of making performance predictions. Within a case study, we show that the resulting prediction accuracy can be sufficient to support the evaluation of architectural design decisions.

https://sdq.ipd.kit.edu/uploads/media/wosp08f-becker_04.pdf

Model-Based performance prediction with the palladio component model

Quantitative prediction of quality properties (i.e. extra-functional 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 error-prone 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.

/pdf/automatically-improve-software-architecture-models-for-4e5v0oyxn9.pdf

Automatically improve software architecture models for performance, reliability, and cost using evolutionary algorithms

Designing software architectures that exhibit a good trade-off between multiple quality attributes is hard. Even with a given functional design, many degrees of freedom in the software architecture (e.g. component deployment or server configuration) span a large design space. In current practice, software architects try to find good solutions manually, which is time-consuming, can be error-prone and can lead to suboptimal designs. We propose an automated approach guided by architectural tactics to search the design space for good solutions. Our approach applies multi-objective evolutionary optimization to software architectures modelled with the Palladio Component Model. Software architects can then make well-informed trade-off decisions and choose the best architecture for their situation. To validate our approach, we applied it to the architecture models of two systems, a business reporting system and an industrial control system from ABB. The approach was able to find meaningful trade-offs leading to significant performance improvements or costs savings. The novel use of tactics decreased the time needed to find good solutions by up to 80%.

/pdf/peropteryx-automated-application-of-tactics-in-multi-wj7gj0z79l.pdf

Heiko Koziolek

Papers

The Palladio component model for model-driven performance prediction

Performance evaluation of component-based software systems: A survey

Model-Based performance prediction with the palladio component model

Automatically improve software architecture models for performance, reliability, and cost using evolutionary algorithms

PerOpteryx: automated application of tactics in multi-objective software architecture optimization