ScienceDirect® Home Skip Main Navigation Links
You have guest access to ScienceDirect. Find out more.
 
Home
Browse
My Settings
Alerts
Help
 Quick Search
 Search tips (Opens new window)
    Clear all fields    
Performance Evaluation
Volume 58, Issue 4, December 2004, Pages 391-412
 
Font Size: Decrease Font Size  Increase Font Size
 Abstract - selected
Article
Purchase PDF (213 K)

Article Toolbox
 
 
 
Related Articles in ScienceDirect
View More Related Articles
 
View Record in Scopus
 
doi:10.1016/j.peva.2004.04.003    
How to Cite or Link Using DOI (Opens New Window)

Copyright © 2004 Elsevier B.V. All rights reserved.

An analytical approach to architecture-based software performance and reliability prediction

Purchase the full-text article



References and further reading may be available for this article. To view references and further reading you must purchase this article.

Swapna S. Gokhalea, Corresponding Author Contact Information, E-mail The Corresponding Author, W. Eric Wongb, J.R. Horganc and Kishor S. Trivedid

aDepartment of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA

bDepartment of Computer Science, University of Texas at Dallas, Richardson, TX 7508, USA

cApplied Research, Telcordia Technologies, Morristown, NJ 07960, USA

dDepartment of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA


Received 4 January 2001; 
revised 14 April 2004. 
Available online 11 September 2004.

Abstract

Conventional approaches to analyze the behavior of software applications are black box based, that is, the software application is treated as a whole and only its interactions with the outside world are modeled. The black box approaches ignore information about the internal structure of the application and the behavior of the individual parts. Hence, they are inadequate to model the behavior of a realistic software application, which is likely to be made up of several interacting parts. Architecture-based analysis, which seeks to assess the behavior of a software application taking into consideration the behavior of its parts and the interactions among the parts is thus essential. Most of the research in the area of architecture-based analysis has been devoted to developing analytical models, with very little, if any effort being devoted to how these models might be applied to real software applications. In order to apply these models to software applications, methods must be developed to extract the parameters of the analytical models from information collected during the execution of the application. In this paper, we present an experimental approach to extract the parameters of architecture-based models from code coverage measurements obtained during the execution of the application. To facilitate this, we use a coverage analysis tool called automatic test analyzer in C (ATAC), which is a part of Telcordia Software Visualization and Analysis Toolsuite (TSVAT) developed at Telcordia Technologies. We demonstrate the approach by predicting the performance and reliability of an application called Symbolic Hierarchical Automated Reliability Predictor (SHARPE), which has been widely used to solve stochastic models of reliability, performance and performability.

Keywords: Markov chain; Semi-Markov process; Software architecture; Software performance; Software reliability

Article Outline

1. Introduction
2. Description and analyses of state-based models
2.1. Architecture of the application
2.2. Failure behavior of the components
2.3. Method of analyses
3. Experimental approach
3.1. Selecting the application
3.2. Executing the application
3.3. Determining failure behavior of the components
3.4. Determining the architecture of the application
4. Results and analyses
5. Sensitivity analysis
6. Conclusions and future research
Acknowledgements
References
Vitae







Corresponding Author Contact InformationCorresponding author.

Performance Evaluation
Volume 58, Issue 4, December 2004, Pages 391-412
 
Home
Browse
My Settings
Alerts
Help
Elsevier.com (Opens new window)
About ScienceDirect  |  Contact Us  |  Information for Advertisers  |  Terms & Conditions  |  Privacy Policy
Copyright © 2008 Elsevier B.V. All rights reserved. ScienceDirect® is a registered trademark of Elsevier B.V.