Computer Science ›› 2016, Vol. 43 ›› Issue (6): 156-159, 178.doi: 10.11896/j.issn.1002-137X.2016.06.032

Previous Articles     Next Articles

Software Failure Prediction Model Based on Improved Nonparametric Method

WANG Zong-hui, ZHOU Yong and ZHANG De-ping   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Based on principal component analysis (PCA) and improved N-W nonparametric estimation method (INW),a new software failure prediction model was presented.First of all,through the principal component analysis of training sample set of nonparametric estimation,the input number of nonparametric method was reduced.Then the variancecontribution ratio of PCA was used as the weight of the bandwidth matrix in nonparametric estimation method,the impact of each imput factor on the results was eliminated in a different extent and software failure prediction models were built.Finally,this paper gave example analysis based on one real software failure data set Eclipse JDT.The results show that the failure prediction model based on improved nonparametric method has made further improvement in prediction precision and stability. Within the forecast range of the last ten steps,the average error of predictive value is 16.2575,and the mean square error is 0.0726.

Key words: Software failure,Principal component analysis (PCA),N-W nonparametric estimation,Bandwidth

[1] Wang Q,Wu S J,Li M S.Software defect prediction[J].Journal of Software,2008,19(7):1565-1580(in Chinese) 王青,伍书剑,李明树.软件缺陷预测技术[J].软件学报,2008,19(7):1565-1580
[2] Nagappan N,Ball T,Zeller A.Mining metrics to predict component failures[C]∥28th International Conference on Software Engineering (ICSE).2006:452-461
[3] Liu Ya-nan,Wei Zhi-nong,Zhong Lin-juan,et al.Study on the forecasting model of power supply reliability based on PCA and RVM[J].Power System Protection and Control,2012,40(20):101-105(in Chinese) 刘亚南,卫志农,钟淋涓,等.基于PCA和RVM的电网供电可靠性预测模型研究[J].电力系统保护与控制,2012,40(20):101-105
[4] Catal C.Software fault prediction:A literature review and current trends[J].Expert Systems with Applications,2011,38(4):4626-4636
[5] Sandamali Dharmasena L,Zeephongsekul P.Fitting software reliability growth curves using nonparametric regression methods[J].Statistical Methodology,2010,7(2):109-120
[6] Couto C,Montandon J E,Silva C,et al.Static correspondenceand correlation between field defects and warnings reported by a bug finding tool[J].Software Quality Journal,2013,21(2):241-257
[7] Catal C.Software fault prediction:A literature review and current trends[J].Expert Systems with Applications,2011,38(4):4626-4636
[8] D’Ambros M,Lanza M,Robbes R.An extensive comparison of bug prediction approaches[C]∥7th IEEE Working Conference on Mining Software Repositories (MSR).2010:31-41
[9] Hwang J N,Lay S R,Lippman A.Nonparametric multivariate density estimation:a comparative study[J].IEEE Transactions on Signal Processing,1994,42(10):2795-2810
[10] Couto C,Montandon J E,Silva C.Static correspondence and correlation between field defects and warnings reported by a bug finding tool[J].Software Quality Journal,2013,21(2):241-257
[11] Nagappn N,Ball T.Using software dependencies and churn me-trics to predict field failures:an empirical case study[C]∥First International Symposisum on Empirical Software Engineering and Measurement.2007:364-373
[12] Lee H J,Naish L,Ramamohanarao K.Study of the relationship ofbug consistency with respect to performance of spectra metrics[C]∥2nd IEEE International Conference on Computer Science and Information Technology.2009:501-508
[13] Okutan A,Yildiz O T.Software defect prediction using Bayesian networks[J].Empir Software Eng,2014,19(1):154-181
[14] Couto C,Piresa P.Predicting software defects with causalitytests[J].Journal of Systems and Software,2014,93:154-181
[15] 叶阿忠.非参数和半参数计量经济模型理论[M].北京:科学出版社,2008:30-150

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .