Measurement and Prediction of Software Performance by Models

G.Kasi Reddy

Measurement and Prediction of Software Performance by Models

G.Kasi Reddy * §

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Measurement and Prediction of Software Performance by Models

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Abstract

Software Performance Engineering (SPE) provides a systematic, quantitative approach to constructing software systems that meet performance objectives. It prescribes ways to build performance into new systems rather than try to fix them later. Performance is a pervasive quality of software systems; everything affects it, from the software itself to all underlying layers, such as operating system, middleware, hardware, communication networks, etc. Software Perfor -mance Engineering encompasses efforts to describe and improve performance, with two distinct approaches: an early-cycle predictive model-based approach, and a late-cycle measurement-based approach. Current progress and future trends within these two approaches are described, with a tendency (and a need) for them to converge, in order to cover the entire development cycle.

References

25 Cites in Article

Reference Format

M Moghal,N Hussain,M Mirza,M Mirza,M Choudry (2004). Performance Evaluation and Modeling of Web Server Systems.
C Smith,L Williams (2002). Performance Solutions: A Practical Guide to Creating Responsive, Scalable Software.
M Woodside,G Franks,D Petriu (2007). Future of Software Performance Engineering.
W Hwu,S Ryoo,S.-Z Ueng,J Kelm,I Gelado,S Stone,R Kidd,S Baghsorkhi,A Mahesri,S Tsao,N Navarro,S Lumetta,M Frank,S Patel (2007). parallel programming models for thousand-core microprocessors.
Hans Vandierendonck,Tom Mens (2012). Techniques and Tools for Parallelizing Software.
C Schaefer,V Pankratius,W Tichy (2010). \Engineering parallel applications with tunable architectures.
S Barber (2004). Creating effective load models for performance testing with incomplete empirical data.
Antonia Bertolino,Raffaela Mirandola (2004). Software performance engineering of component-based systems.
Henry Lucas (1971). Performance Evaluation and Monitoring.
Vahid Garousi,Lionel Briand,Yvan Labiche (2006). Traffic-aware stress testing of distributed systems based on UML models.
V Cortellessa,A Di,P Marco,Inverardi (2007). Nonfunctional Modeling and Validation in Model-Driven Architecture.
Chanintorn Jittawiriyanukoon,Hikaru Nakanishi,Yoshikazu Tezuka,Takashi Watanabe (2006). Approximate performance evaluation of parallel processing systems using average concurrency.
D Petriu,C Woodside (2002). Analysing Software Requirements Specifications for Performance.
Murray Woodside,Curtis Hrischuk,Bran Selic,Stefan Bayarov (2001). Automated performance modeling of software generated by a design environment.
Jing Xu,Murray Woodside,Dorina Petriu (2003). Performance Analysis of a Software Design Using the UML Profile for Schedulability, Performance, and Time.
C Szypersky,D Gruntz,S Murer (2002). Component Software: Beyond Object Oriented Programming.
X Wu,M Woodside (2004). Performance Modeling from Software Components.
R Reussner,V Firus,S Becker (2004). Parametric Performance Contracts for Software Components and their Compositionality.
M Woodside,D Petriu,K Siddiqui (2002). Performance-related completions for software specifications.
(2006). Object Management Group, Software Process Engineering Metamodel Specification.
(2005). Modeling and Analysis of Real-Time and Embedded Systems with UML and MARTE.
Connie Smith,Catalina Lladó,Vittorio Cortellessa,Antinisca Marco,Lloyd Williams (2005). From UML models to software performance results.
Murray Woodside,Dorina Petriu,Dorin Petriu,Hui Shen,Toqeer Israr,Jose Merseguer (2005). Performance by unified model analysis (PUMA).
Erika Corona,Filippo Pani (2013). A Review of Lean-Kanban Approaches in the Software Development.
Manoj Kumar Tyagi,M Srinivasan,L Reddy (2013). Design of Traditional/Hybrid Software Project Tracking Technique: State Space Approach.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

G.Kasi Reddy. 2014. "Measurement and Prediction of Software Performance by Models". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C6).

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GJCST Volume 14 Issue C6

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

debugging performance measurement performance prediction SPE UML

Classification

H.1

Submission ReceivedDecember 6, 2013
Peer Review Double Blind
Handling Editor
Accepted January 1, 2014
Published January 15, 2014

Version of record

v1.2

Issue date

September 6, 2014

Language

English

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Article Matrices

Total Score: 147

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: G.Kasi Reddy, Dr. D Sravan Kumar (PhD/Dr. count: 1)

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