Scheme Selection of Software Quality Based on Support Vector Machine

Article Preview

Abstract:

Software Quality scheme selection is an important pre-work of software project, For software quality evaluation index are difficult to quantify and has the characteristics of uncertainty, this paper uses a nonlinear SVM classification algorithm research. Firstly, to build a software quality scheme evaluation index system composed of 12 indicators, and through hierarchical structure analysis model representation; Then, research on support vector machine, using graphic to show that the basic idea, through mathematical model to describe the algorithm, constructed Sigmoid kernel function; Finally, to perform case analysis, through 8 have been evaluated schemes to train, drawn 4 unselected scheme evaluation results. This article will apply SVM to software quality scheme selection, it is SVM a new exploration in the field of software engineering, and to improve software quality has an important significance.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

3312-3315

Citation:

Online since:

March 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] H. Chai, J. Yan, Q. Y. Qin, The model of fuzzy evaluation of software quality based on confidence, Computer engineering and design, vol. 33, no. 2, pp.607-61, (2012).

Google Scholar

[2] R. H. Wang, Improvement of personal credit evaluation model of support vector machine based on consumer credit, Statistics and Decision, vol. 26, no. 11, pp.54-56, (2010).

Google Scholar

[3] Baidu Baike, Support Vector Machine, " http: /baike. baidu. com/link, url=dDO8Rlpso MiM9OLum61vrW1LasdHZ2NOnzu5DopyD4FwQFLhiCOZz7HPosS9tYXs, 2013-12-08.

Google Scholar

[4] J. F. Su, The project management of the software development process quality, Heilongjiang Science and Technology Information, vol. 13, no. 2, pp.118-119, (2009).

Google Scholar

[5] X. S. Wu, Discussion on software quality assurance of software development process, Study of Science and Engineering at RTVU, vol. 32, no. 1, pp.48-50, (2010).

Google Scholar

[6] Z. Y. Zhang, Y. X. Liu, Research on land resource eco-security assessment based on Support Vector Machines, Computer Engineering and Applications, vol. 45, no. 10, pp.245-248, (2009).

Google Scholar

[7] H. J. Yan, J. Qin, Trojan Horse Detection Method Based on Nonlinear SVM Model, Computer Engineering, vol. 37, no. 8, pp.121-123, (2011).

Google Scholar

[8] K. Zhang, Based on Support Vector Machine (SVM) Personal Credit Evaluation, " Master, s Liaoning Technical University, (2010).

Google Scholar

[9] M. Elangovan, V. Sugumaran, K.I. Ramachandran, S. Ravikumar, Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool, Expert Systems with Applications, vol. 38, no. 12, pp.15202-15207, (2011).

DOI: 10.1016/j.eswa.2011.05.081

Google Scholar

[10] Interactive Encyclopedia, Evaluation, http: /www. baike. com/wiki Evaluation, 2013-12-08.

Google Scholar