Extended Implementation of Change Impact Analysis Model-Based Framework to Enhance Predicting the Effect of a Change of Service in a Grid Environment

Obeten Obi Ekabua

doi:doi:10.11648/j.ajsea.20130206.12

| Peer-Reviewed

Extended Implementation of Change Impact Analysis Model-Based Framework to Enhance Predicting the Effect of a Change of Service in a Grid Environment

Obeten Obi Ekabua

Published in American Journal of Software Engineering and Applications (Volume 2, Issue 6)

Received: 24 October 2013 Published: 20 November 2013

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Continuous monitoring of changes to utility services and products in a distributed information system is an interesting issue in software engineering. These changes affect the semantics and structural complexity of the system, as a change to one part will in most cases, result in changes to other parts. Therefore, in design and redesign for customization, predicting this change presents a significant challenge. Changes are intended to fix faults, improve or update products and services. Lack of validated, widely accepted, and adopted tools for planning, estimating, and performing maintenance contributes to the problem. One effective way of assessing changeability effect is to assess the impact of changes through a well validated model and framework. This research paper is an extended report on the implementation of a change propagation framework, together with it’s associated change impact analysis factor adaptation model, and a fault and failure assumption model to predict the effect of a change of a service in a grid environment. While implementing the framework, data was collected for three hypothetical years, thus helping to predict the next two (2) years consecutively. Significant results corresponding to the impact analysis factor were obtained showing the viable practicality of the use of Bayesian statistics (as against unreported regression method) satisfying best-fit prediction. We conclude that, the higher the number of dependent services on a faulty service requiring a change, the higher the impact due to fault propagation.

Published in	American Journal of Software Engineering and Applications (Volume 2, Issue 6)
DOI	10.11648/j.ajsea.20130206.12
Page(s)	133-140
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Change Impact Analysis, Service Provisioning, Software Metrics, Service Maintenance, Bayesian Statistics, Grid Environment

References

[1]	Fenton, N. , (1994): Software Measurement: A Necessary Scientific Basis. IEEE Transactions on software Engineering. Vol. 20, No. 3
[2]	Bennett, K., Munro, M., Gold, N., Layzell, P., Mehandjiev, N., Budgne, D., Brereton, P. (2001): An architectural model for service-based flexible software. In Proceeding of the 25th Annual International Conference on Computer Software and Application (COMPSAC 2001) IEEE Computer Society, 137-142
[3]	Liu, X., Yang, H., and Zedan, H. (1997): Formal Methods for the Re-engineering of Computing Systems. In Proceedings of the 21st IEEE International Conference on Computer Software and Application (COMPSAC’97), pages 409-411, Washington, D.C. IEEE Computer Society.
[4]	Zhou, S., Zedan, H., and Cau, A.(1999): A framework for Analysing the Effect of ‘Change’ In Legacy Code. In IEEE 15th International Conference in Software maintanance (ICSM’99), pp 411.
[5]	Cotroneo, D., Di Flora, C. And Russo, S.(2003): Improving Dependability of Service Oriented Architecture for Pervasive Computing. Proceedings of the 8th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems. ISBN 0-7695-1929-6/03.
[6]	Davies, N., Gellersen, H. W. (2002): Beyond Prototypes: Challenges in deploying Ubiquitous Systems. In IEEE Pervasive Computing 1(1): pp26-35
[7]	Turnitsa, C. D. (2005): Extending the levels of Conceptual Interoperability Model. IEEE proceedings of Summer Computer Simulation Conference. IEEE Computer Society Press.
[8]	Lee, M.L. (1998): Change Impact Analysis of Object-Oriented Software. Technical Report ISE-TR-99-06, George Mason University.
[9]	Hao, H. (2003): What is Service-Oriented Architecture. CTO of SoftTouch Information Technology Pty. webservices.xml.com
[10]	David, S. and Lawrence, W. (2004): Understanding Service-Oriented Architecture. .NET Architecture Centre. Microsoft Architect Journal, January.
[11]	IEEE Standard (1990). IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries. New York, NY.
[12]	Hayes, J. H., Patel, S. C., and Zhao, L.(2004): A Metrics-Based software Maintenance Effort Model. Proceedings of IEEE 8th European Conference on Software Maintenance and Reengineering (CSMR’04).
[13]	Kabaili, H., Keller, R. K., and Lustman, F. (2001): A Cohesion as Changeability Indicators in Object-Oriented Systems. Proceedings of IEEE 5th European Conference on Software Maintenance and Reengineering.
[14]	Chaumum, M., Kabaili, H., Keller, R., and Lustman, F. (1999): A Change Impact Model for Changeability Assessment in Object-Oriented Software Systems. Proceedings of IEEE third European Conference on Software Maintenance and Reengineering.
[15]	Elish, M. O. and Rine, D. (2003): Investigation of Metrics for Object Oriented Design Logical Stability. Proceedings of 7th European Conference on Software Maintenance and Reengineering. pp.193-200.
[16]	Ekabua, O. O., Olugbara, O. O. and Adigun, M. O. 2007: A Generic Change Propagation Framework to Enhance Service Provisioning in a Grid Environment. Asian Journal of Information Technology, 6(10): 1015-1019, ISSN: 1682-3915
[17]	Ekabua, O. O. and Adigun, M. O. (2009): Experienced Report on Assessing and Evaluating Change Impact Analysis through a Framework and Associated Models. Journal of Information Science and Engineering. 25, 363-373.

Cite This Article

Plain Text BibTeX RIS

APA Style

Obeten Obi Ekabua. (2013). Extended Implementation of Change Impact Analysis Model-Based Framework to Enhance Predicting the Effect of a Change of Service in a Grid Environment. American Journal of Software Engineering and Applications, 2(6), 133-140. https://doi.org/10.11648/j.ajsea.20130206.12

Copy | Download

ACS Style

Obeten Obi Ekabua. Extended Implementation of Change Impact Analysis Model-Based Framework to Enhance Predicting the Effect of a Change of Service in a Grid Environment. Am. J. Softw. Eng. Appl. 2013, 2(6), 133-140. doi: 10.11648/j.ajsea.20130206.12

Copy | Download

AMA Style

Obeten Obi Ekabua. Extended Implementation of Change Impact Analysis Model-Based Framework to Enhance Predicting the Effect of a Change of Service in a Grid Environment. Am J Softw Eng Appl. 2013;2(6):133-140. doi: 10.11648/j.ajsea.20130206.12

Copy | Download

@article{10.11648/j.ajsea.20130206.12,
  author = {Obeten Obi Ekabua},
  title = {Extended Implementation of Change Impact Analysis Model-Based Framework to Enhance Predicting the Effect of a Change of Service in a Grid Environment},
  journal = {American Journal of Software Engineering and Applications},
  volume = {2},
  number = {6},
  pages = {133-140},
  doi = {10.11648/j.ajsea.20130206.12},
  url = {https://doi.org/10.11648/j.ajsea.20130206.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20130206.12},
  abstract = {Continuous monitoring of changes to utility services and products in a distributed information system is an interesting issue in software engineering. These changes affect the semantics and structural complexity of the system, as a change to one part will in most cases, result in changes to other parts. Therefore, in design and redesign for customization, predicting this change presents a significant challenge. Changes are intended to fix faults, improve or update products and services. Lack of validated, widely accepted, and adopted tools for planning, estimating, and performing maintenance contributes to the problem. One effective way of assessing changeability effect is to assess the impact of changes through a well validated model and framework. This research paper is an extended report on the implementation of a change propagation framework, together with it’s associated change impact analysis factor adaptation model, and a fault and failure assumption model to predict the effect of a change of a service in a grid environment. While implementing the framework, data was collected for  three hypothetical years, thus helping to predict the next two (2) years consecutively. Significant results corresponding to the impact analysis factor were obtained showing the viable practicality of the use of Bayesian statistics (as against unreported regression method) satisfying best-fit prediction. We conclude that, the higher the number of dependent services on a faulty service requiring a change, the higher the impact due to fault propagation.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Extended Implementation of Change Impact Analysis Model-Based Framework to Enhance Predicting the Effect of a Change of Service in a Grid Environment
AU  - Obeten Obi Ekabua
Y1  - 2013/11/20
PY  - 2013
N1  - https://doi.org/10.11648/j.ajsea.20130206.12
DO  - 10.11648/j.ajsea.20130206.12
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 133
EP  - 140
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20130206.12
AB  - Continuous monitoring of changes to utility services and products in a distributed information system is an interesting issue in software engineering. These changes affect the semantics and structural complexity of the system, as a change to one part will in most cases, result in changes to other parts. Therefore, in design and redesign for customization, predicting this change presents a significant challenge. Changes are intended to fix faults, improve or update products and services. Lack of validated, widely accepted, and adopted tools for planning, estimating, and performing maintenance contributes to the problem. One effective way of assessing changeability effect is to assess the impact of changes through a well validated model and framework. This research paper is an extended report on the implementation of a change propagation framework, together with it’s associated change impact analysis factor adaptation model, and a fault and failure assumption model to predict the effect of a change of a service in a grid environment. While implementing the framework, data was collected for  three hypothetical years, thus helping to predict the next two (2) years consecutively. Significant results corresponding to the impact analysis factor were obtained showing the viable practicality of the use of Bayesian statistics (as against unreported regression method) satisfying best-fit prediction. We conclude that, the higher the number of dependent services on a faulty service requiring a change, the higher the impact due to fault propagation.
VL  - 2
IS  - 6
ER  -

Copy | Download

Author Information

Obeten Obi Ekabua

Department of Computer Science, North-West University, Mafikeng Campus, Mmabatho 2735, South Africa

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Obeten Obi Ekabua. (2013). Extended Implementation of Change Impact Analysis Model-Based Framework to Enhance Predicting the Effect of a Change of Service in a Grid Environment. American Journal of Software Engineering and Applications, 2(6), 133-140. https://doi.org/10.11648/j.ajsea.20130206.12

Copy | Download

ACS Style

Obeten Obi Ekabua. Extended Implementation of Change Impact Analysis Model-Based Framework to Enhance Predicting the Effect of a Change of Service in a Grid Environment. Am. J. Softw. Eng. Appl. 2013, 2(6), 133-140. doi: 10.11648/j.ajsea.20130206.12

Copy | Download

AMA Style

Obeten Obi Ekabua. Extended Implementation of Change Impact Analysis Model-Based Framework to Enhance Predicting the Effect of a Change of Service in a Grid Environment. Am J Softw Eng Appl. 2013;2(6):133-140. doi: 10.11648/j.ajsea.20130206.12

Copy | Download

@article{10.11648/j.ajsea.20130206.12,
  author = {Obeten Obi Ekabua},
  title = {Extended Implementation of Change Impact Analysis Model-Based Framework to Enhance Predicting the Effect of a Change of Service in a Grid Environment},
  journal = {American Journal of Software Engineering and Applications},
  volume = {2},
  number = {6},
  pages = {133-140},
  doi = {10.11648/j.ajsea.20130206.12},
  url = {https://doi.org/10.11648/j.ajsea.20130206.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20130206.12},
  abstract = {Continuous monitoring of changes to utility services and products in a distributed information system is an interesting issue in software engineering. These changes affect the semantics and structural complexity of the system, as a change to one part will in most cases, result in changes to other parts. Therefore, in design and redesign for customization, predicting this change presents a significant challenge. Changes are intended to fix faults, improve or update products and services. Lack of validated, widely accepted, and adopted tools for planning, estimating, and performing maintenance contributes to the problem. One effective way of assessing changeability effect is to assess the impact of changes through a well validated model and framework. This research paper is an extended report on the implementation of a change propagation framework, together with it’s associated change impact analysis factor adaptation model, and a fault and failure assumption model to predict the effect of a change of a service in a grid environment. While implementing the framework, data was collected for  three hypothetical years, thus helping to predict the next two (2) years consecutively. Significant results corresponding to the impact analysis factor were obtained showing the viable practicality of the use of Bayesian statistics (as against unreported regression method) satisfying best-fit prediction. We conclude that, the higher the number of dependent services on a faulty service requiring a change, the higher the impact due to fault propagation.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Extended Implementation of Change Impact Analysis Model-Based Framework to Enhance Predicting the Effect of a Change of Service in a Grid Environment
AU  - Obeten Obi Ekabua
Y1  - 2013/11/20
PY  - 2013
N1  - https://doi.org/10.11648/j.ajsea.20130206.12
DO  - 10.11648/j.ajsea.20130206.12
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 133
EP  - 140
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20130206.12
AB  - Continuous monitoring of changes to utility services and products in a distributed information system is an interesting issue in software engineering. These changes affect the semantics and structural complexity of the system, as a change to one part will in most cases, result in changes to other parts. Therefore, in design and redesign for customization, predicting this change presents a significant challenge. Changes are intended to fix faults, improve or update products and services. Lack of validated, widely accepted, and adopted tools for planning, estimating, and performing maintenance contributes to the problem. One effective way of assessing changeability effect is to assess the impact of changes through a well validated model and framework. This research paper is an extended report on the implementation of a change propagation framework, together with it’s associated change impact analysis factor adaptation model, and a fault and failure assumption model to predict the effect of a change of a service in a grid environment. While implementing the framework, data was collected for  three hypothetical years, thus helping to predict the next two (2) years consecutively. Significant results corresponding to the impact analysis factor were obtained showing the viable practicality of the use of Bayesian statistics (as against unreported regression method) satisfying best-fit prediction. We conclude that, the higher the number of dependent services on a faulty service requiring a change, the higher the impact due to fault propagation.
VL  - 2
IS  - 6
ER  -

Copy | Download