Configurable software systems allow stakeholders to derive variants by selecting software and/or hardware features. Performance analysis of feature-based systems has been of large interest in the last few years, however a major research challenge is still to conduct such analysis before achieving full knowledge of the system, namely under a certain degree of uncertainty. In this paper we present an approach to analyze the correlation between selection of features embedding uncertain parameters and system performance. In particular, we provide best and worst case performance bounds on the basis of selected features and, in cases of wide gaps among these bounds, we carry on a sensitivity analysis process aimed at taming the uncertainty of parameters. The application of our approach to a case study in the e-health domain demonstrates how to support stakeholders in the identification of system variants that meet performance requirements.

Performance-based selection of software and hardware features under parameter uncertainty

Trubiani Catia;
2014

Abstract

Configurable software systems allow stakeholders to derive variants by selecting software and/or hardware features. Performance analysis of feature-based systems has been of large interest in the last few years, however a major research challenge is still to conduct such analysis before achieving full knowledge of the system, namely under a certain degree of uncertainty. In this paper we present an approach to analyze the correlation between selection of features embedding uncertain parameters and system performance. In particular, we provide best and worst case performance bounds on the basis of selected features and, in cases of wide gaps among these bounds, we carry on a sensitivity analysis process aimed at taming the uncertainty of parameters. The application of our approach to a case study in the e-health domain demonstrates how to support stakeholders in the identification of system variants that meet performance requirements.
Software Architectures; Performance Analysis; Feature Selection; Uncertainty
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12571/7324
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