Study on the Modeling Method of Software Process Based on Timing and Parallel Automata

Article Preview

Abstract:

The finite automata theory extended and then the timing parallel automata theory is got and applied in the software process modeling. The establishment of group software process model is on the basis of timing parallel automata which realize the activity planning, resource allocation and progress control of process. The process model has been checked rationality and the rationality definition and check rules have been given. The process modeling method in this paper is intuitive, easy to understand and could describe the dynamic change of process, and also present the concurrent activity and provide the effective support to parallel work and cooperative work.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 765-767)

Pages:

1537-1540

Citation:

Online since:

September 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] LIU Shi-lan, REN Hao, The Comparison Between Total Quality Management(TQM) and Traditional Management(TM), Commercial Research, vol. 337, pp.122-124, (2006).

Google Scholar

[2] HE Zhen, ZHOU Shan-zhong, Process Management for Continuous Quality Improvement, Industrial Engineering Journal, vol. 5(8), pp.38-41, (2005).

Google Scholar

[3] HE Zhen, LU Jin, LIU Xiao-liang, HE Shu-guang, Design And Development of the Integrated Quality Management System IQMS2. 0, Industrial Engineering Journal, vol. 10(6), pp.54-58, (2007).

Google Scholar

[4] YUAN Yi-jun; LIU Hao, Service outsourcing and promotion of technical innovative efficiency in manufacturing industry, Journal of Dalian University of Technology(Social Sciences), vol. 28(4), pp.1-6, (2007).

Google Scholar

[5] CHEN Guo quan, MA Meng, Studies on the process model of organizational learning, JOURNAL OF MANEGEMENT SCIENCES IN CHINA, vol. 3(3), pp.15-23, (2000).

Google Scholar

[6] Edmondson, Mo ingeon, Organizational learning and competitive advantage, New York: Mc Graw-Hill, pp.1-10, (1997).

Google Scholar

[7] DAI Wan-wen, ZHAO Shu-ming, Steve F Foster, Study on a dynamic model of organizational learning processes under complex system, Studies in Science of Science, vol. 24, pp.217-224, (2006).

Google Scholar

[8] Lenstra J K, Rinnooy K, 1981, Complexity of vehicle routing and scheduling problem, Networks, Vol. 11, pp.221-227.

Google Scholar

[9] Davis L, 1991, Handbook of genetic algorithm, New York: Van Nostrand Reinhold.

Google Scholar

[10] Goldberg DE, 1989, Genetic algorithms in search, optimization, and machine learning, Reading: Addison-Wesley.

Google Scholar

[11] Holland J.H., 1975, Adaptation in Natural and Artificial Systems, Ann Arbor: University of Michigan Press.

Google Scholar

[12] C. Sangit, C. Cecilia, A.L. Lucy, 1996, Genetic algorithms and traveling salesman problems, European Journal of Operational Research, Vol. 93, p.490–510.

DOI: 10.1016/0377-2217(95)00077-1

Google Scholar

[13] Zhao He, Du Duanfu, 1998, The Operators Choice and Design of Genetic Algorithm for TSP, Systems Engineering: Theory & Practice, Vol. 18, p.62–65.

Google Scholar