Computer Science

Permanent URI for this collectionhttps://hdl.handle.net/10679/43

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Browsing by Author "Aksit, M."

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    Availability analysis of software architecture decomposition alternatives for local recovery
    (2017) Sözer, Hasan; Stoelinga, M.; Boudali, H.; Aksit, M.; Computer Science; SÖZER, Hasan
    We present an efficient and easy-to-use methodology to predict—at design time—the availability of systems that support local recovery. Our analysis techniques work at the architectural level, where the software designer simply inputs the software modules’ decomposition annotated with failure and repair rates. From this decomposition, we automatically generate an analytical model (a continuous-time Markov chain), from which an availability measure is then computed, in a completely automated way. A crucial step is the use of intermediate models in the input/output interactive Markov chain formalism, which makes our techniques efficient, mathematically rigorous, and easy to adapt. In particular, we use aggressive minimization techniques to keep the size of the generated state spaces small. We have applied our methodology on a realistic case study, namely the MPlayer open-source software. We have investigated four different decomposition alternatives and compared our analytical results with the measured availability on a running MPlayer. We found that our predicted results closely match the measured ones .
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    Guiding architects in selecting architectural evolution alternatives
    (Springer Science+Business Media, 2011) Ciraci, S.; Sözer, Hasan; Aksit, M.; Computer Science; SÖZER, Hasan
    Although there exist methods and tools to support architecture evolution, the derivation and evaluation of alternative evolution paths are realized manually. In this paper, we introduce an approach, where architecture specification is converted to a graph representation. Based on this representation, we automatically generate possible evolution paths, evaluate quality attributes for different architectural configurations, and optimize the selection of a particular path accordingly. We illustrate our approach by modeling the software architecture evolution of a crisis management system.