single-jc.php

JACIII Vol.23 No.2 pp. 340-344
doi: 10.20965/jaciii.2019.p0340
(2019)

Short Paper:

Efficient Prediction Method of Defect of Monitor Configuration Software

Yan Wang

Open Education Institute, Chengdu Radio and TV University
No.7, Section 1, Jianshe North Road, Chenghua District, Chengdu, Sichuan 610051, China

Received:
April 9, 2018
Accepted:
January 24, 2019
Published:
March 20, 2019
Keywords:
monitor configuration software, defect prediction, metric element, genetic optimization
Abstract
Efficient Prediction Method of Defect of Monitor Configuration Software

Monitoring configuration software defect prediction steps

In order to solve the problem of low efficiency in software operation, we need to research the defect prediction of monitoring configuration software. The current method has the low efficiency in the defect prediction of software. Therefore, this paper proposed the software defect prediction method based on genetic optimization support vector machines. This method carried out feature selection for the measure of complexity of software, and built software defect prediction model of genetic optimized support vector machine, and completed the research on the efficient prediction method of software defects. Experimental results show that the proposed method improves the quality of software effectively.

Cite this article as:
Y. Wang, “Efficient Prediction Method of Defect of Monitor Configuration Software,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.2, pp. 340-344, 2019.
Data files:
References
  1. [1] G. Yang, Y. Zhang, J. Yang, G. Li, Z. Dong, S. Wang, C. Feng, and Q. Wang, “Automated classification of brain images using wavelet-energy and biogeography-based optimization,” Multimedia Tools and Applications, Vol.75, No.23, pp. 15601-15617, 2016.
  2. [2] M. Lanza, A. Mocci, and L. Ponzanelli, “The Tragedy of Defect Prediction, Prince of Empirical Software Engineering Research,” IEEE Software, Vol.33, No.6, pp. 102-105, 2016.
  3. [3] Z. A. Rana, M. A. Mian, and S. Shamail, “Improving Recall of software defect prediction models using association mining,” Knowledge-Based Systems, Vol.90, No.C, pp. 1-13, 2015.
  4. [4] F. Zhang, A. E. Hassan, S. Mcintosh, and Y. Zou, “The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction Models,” IEEE Trans. on Software Engineering, Vol.43, No.5, pp. 476-491, 2017.
  5. [5] G. Yang, W. Tan, H. Jin, T. Zhao, and L. Tu, “Review wearable sensing system for gait recognition,” Cluster Computing, pp. 1-9, 2018.
  6. [6] X. Chen, C. He, Y. Wang, et al., “HFS: A hybrid feature selection approach for software defect prediction,” Application Research of Computers, Vol.33, No.6, pp. 1758-1761, 2016.
  7. [7] X. Dai and Y. Mao, “Research on software defect prediction based on integrated sampling and ensemble learning,” Computer Engineering and Science, Vol.37, No.5, pp. 930-936, 2015.
  8. [8] Q. Meng and X. Ma, “Software defect prediction using rough sets and support vector machine,” Computer Engineering and Science, Vol.37, No.1, pp. 93-98, 2015.
  9. [9] Z. Alfughi, S. Rahnamayan, and B. Yilbas, “Multi-Objective Solar Farm Design Based on Parabolic Collectors,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.2, pp. 256-270, 2018.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Sep. 09, 2019