NDLI: Quantitative Analysis for Non-linear System Performance Data Using Case-Based Reasoning

Content Provider	IEEE Xplore Digital Library
Author	Keung, J.W. Thong Nguyen
Copyright Year	2010
Abstract	Effective software architecture evaluation methods are essential in today’s system development for mission critical systems. We have previously developed MEMS and a set of test statistics for evaluating middleware architectures, which proven an effective assessment of important quality attributes and their characterizations. We have observed it is common that many system performance response data are not of linear nature, where using linear modeling is not feasible in these scenarios for system performance predictions. To provide an alternative quantitative assessment on the system performance using actual runtime datasets, we developed a set of non-linear analysis procedure based on Case-based Reasoning (CBR), a machine learning method widely used in another disciplines of Software Engineering. Experiments were carried out based on actual runtime performance datasets. Results confirm that our non-linear analysis method CBR4MEMS produced accurate performance predictions and outperformed linear approaches. Our approach utilizing CBR to enable performance assessments on non-linear datasets, a major step forward to support software architecture evaluation.
Starting Page	346
Ending Page	355
File Size	1050207
Page Count	10
File Format	PDF
ISBN	9781424488315
ISSN	15301362
DOI	10.1109/APSEC.2010.47
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-30
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Micromechanical devices Predictive models Cognition Measurement Middleware System performance Data models Software Architecture Evaluation Software Measurement Case-based Reasoning
Content Type	Text
Resource Type	Article

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