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Issue No.04 - October-December (2010 vol.3)
pp: 338-352
Lianzhang Zhu , China University of Petroleum (East China), Dongying
There are at least two challenges with quality management of service-oriented architecture based web service systems: 1) how to link its technical capabilities with customer's needs explicitly to satisfy customers' functional and nonfunctional requirements; and 2) how to determine targets of web service design attributes. Currently, the first issue is not addressed and the second one is dealt with subjectively. Quality Function Deployment (QFD), a quality management system, has found its success in improving quality of complex products although it has not been used for developing web service systems. In this paper, we analyze requirements for web services and their design attributes, and apply the QFD for developing web service systems by linking quality of service requirements to web service design attributes. A new method for technical target setting in QFD, based on an artificial neural network, is also presented. Compared with the conventional methods for technical target setting in QFD, such as benchmarking and the linear regression method, which fail to incorporate nonlinear relationships between design attributes and quality of service requirements, it sets up technical targets consistent with relationships between quality of web service requirements and design attributes, no matter whether they are linear or nonlinear.
Web service system, service quality management, Bayesian regularized neural network, quality function deployment (QFD), technical targets setting.
Lianzhang Zhu, "Technical Target Setting in QFD for Web Service Systems Using an Artificial Neural Network", IEEE Transactions on Services Computing, vol.3, no. 4, pp. 338-352, October-December 2010, doi:10.1109/TSC.2010.45
[1] M. Miller, Cloud Computing: Web-Based Applications That Change the Way You Work and Collaborate Online. Que, 2008.
[2] E. Newcomer and G. Lomow, Understanding SOA with Web Services. Addison-Wesley Professional, 2004.
[3] "Enterprise Software Customer Survey 2007," http://www. pdfEnterprise-Software-Customer-Survey-2007.pdf , 2009.
[4] "Enterprise Software Customer Survey 2008," http://www. mckinsey_software2008_survey. pdf, 2009.
[5] D. Sanderson, Programming Google App Engine: Rough Cuts Version. O'Reilly, 2008.
[6] M.H. Ibrahim, K. Holley, N.M. Josuttis, B. Michelson, D. Thomas, and J. deVadoss, "The Future of SOA: What Worked, What Didn't, and Where Is It Going from Here?" Proc. 22nd ACM SIGPLAN Conf. Object-Oriented Programming Systems and Applications Companion (OOPSLA '07), pp. 1034-1037, Oct. 2007.
[7] X.F. Liu and L. Zhu, "Design of SOA Based Web Service Systems Using QFD for Satisfaction of Quality of Service Requirements," Proc. IEEE Int'l Conf. Web Services (ICWS), pp. 567-574, 2009.
[8] Y. Akao, Quality Function Deployment. Productivity Press, 1990.
[9] M. Erder and P. Pureur, "QFD in the Architecture Development Process," IT Professional, vol. 5, no. 6, pp. 44-52, 2003.
[10] S. Haag, M.K. Raja, and L.L. Schkade, "Quality Function Deployment—Usage in Software Development," Comm. ACM, vol. 39, no. 1, pp. 42-49, 1996.
[11] J.R. Hauser and D. Clausing, "The House of Quality," Harvard Business Rev., vol. 66, no. 3, pp. 63-73, 1988.
[12] X.F. Liu, P. Inuganti, and K. Noguchi, "Technical Target Setting in Time-Stamped Quality Function Deployment," Total Quality Management and Business Excellence, vol. 17, no. 2, pp. 149-177, Mar. 2006.
[13] X.F. Liu, K. Noguchi, A. Dhungana, V.V.N.S.N. Srirangam A., and P. Inuganti, "A Quantitative Approach for Setting Technical Targets Based on Impact Analysis in Software Quality Function Deployment (SQFD)," Software Quality J., vol. 14, no. 2, pp. 113-134, June 2006.
[14] X.F. Liu, Y. Sun, G. Kane, Y. Kyoya, and K. Noguchi, "Business-Oriented Software Process Improvement Based on CMM Using QFD," J. Software Process Improvement and Practice, vol. 11, no. 6, pp. 573-589, Nov./Dec. 2006.
[15] M. Yang, Y. Li, S. Li, and P. Li., "ANN-Based Fuzzy Reasoning to Determine the Importance of Technical Requirements in QFD," Proc. Fourth Int'l Conf. Wireless Comm., Networking and Mobile Computing (WiCOM '08), pp. 1-5, 2008.
[16] F. Siraj et al., "Quality Function Deployment Analysis Based on Neural Network and Statistical Results," Int'l J. Simulation: Systems, Science and Technology, vol. 9, no. 2, pp. 73-81, May 2008.
[17] S. Zhang, Y. Dong, B. Pei, and X. Yang, "Research on the Optimization Method of Logistics Service Capacity Based on Dynamic QFD," Proc. Int'l Conf. Intelligent Computation Technology and Automation, pp. 664-668, Oct. 2008.
[18] S. Myint, "A Framework of an Intelligent Quality Function Deployment (IQFD) for Discrete Assembly Environment," Computers and Industrial Eng., vol. 45, pp. 269-283, 2003.
[19] Y.-C. Chou, "Applying Neural Networks in Quality Function Deployment Process for Conceptual Design," J. Chinese Inst. of Industrial Engineers, vol. 21, no. 6, pp. 587-596, 2004.
[20] D. Paul, S.R. Bhadra Chaudhuri, D. Mukherjee, and S.N. Mandal, "A Soft Computing Model for Optimizing Significant Parameters of Insolation Distribution in BIPV Application," Proc. Int'l Conf. Computer Science and Information Technology, Aug. 2008.
[21] K.M. Daws, Z.A. Ahmed, and A.A. Moosa, "An Intelligent Quality Function Deployment (IQFD) for Manufacturing Process Environment," Jordan J. Mechanical and Industrial Eng., vol. 3, no. 1, pp. 23-30, Mar. 2009.
[22] Z.-H. Ren, B.-C. Wang, and B.-C. Wen, "A Model of HoQ Templet Automatic Generation Based on RBF-ANN," Proc. Third Int'l Conf. Machine Learning Cybernetics, pp. 3497-3500, 2004.
[23] S. Venkatachalam, C. Arumugam, K. Raja, and V. Selladurai, "Quality Function Deployment in Agile Parallel Machine Scheduling through Neural Network Technique," Asian J. Scientific Research, vol. 1, no. 2, pp. 146-152, 2008.
[24] L. O'Brien, L. Bass, and P. Merson, "Quality Attributes and Service-Oriented Architectures," CMU/SEI-2005-TN-014, Sept. 2005.
[25] H. Demuth and M. Beale, Neural Network Toolbox User's Guide Version 4. The MathWorks, Inc., 2002.
[26] T. Chen and H. Chen, "Universal Approximation to Nonlinear Operators by Neural Networks with Arbitrary Activation Functions and Its Application to Dynamical Systems," IEEE Trans. Neural Networks, vol. 6, no. 4, pp. 911-917, July 1995.
[27] R. Neal, Bayesian Learning for Neural Networks. Springer, 1996.
[28] J. Lampinen and A. Vehtari, "Bayesian Approach for Neural Networks—Review and Case Studies," Neural Networks, vol. 14, no. 3, pp. 257-274, Apr. 2001.
[29] M. Matteucci and D. Spadoni, "Evolutionary Learning of Rich Neural Networks in the Bayesian Model Selection Framework," Int'l J. Applied Math. and Computer Science, vol. 14, no. 3, pp. 423-440, 2004.
[30] F.D. Foresee and M.T. Hagan, "Gauss-Newton Approximation to Bayesian Regularization," Proc. Int'l Joint Conf. Neural Networks, pp. 1930-1935, 1997.
[31] D. Nguyen and B. Widrow, "Improving the Learning Speed of 2-Layer Neural Networks by Choosing Initial Values of the Adaptive Weights," Proc. Int'l Joint Conf. Neural Networks (IJCNN), vol. 3, pp. 21-26, July 1990.
[32] P.A.D. Castro and F.J. Von Zuben, "Bayesian Learning of Neural Networks by Means of Artificial Immune Systems," Proc. Int'l Joint Conf. Neural Networks, July 2006.
[33] M.S. Khan and P. Coulibaly, "Streamflow Forecasting with Uncertainty Estimate Using Bayesian Learning for ANN," Proc. Int'l Joint Conf. Neural Networks, July/Aug. 2005.
[34] K.K. Aggarwal, Y. Singh, P. Chandra, and M. Puri, "Bayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points," J. Computer Sciences, vol. 1, no. 4, pp. 505-509, 2005.
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