Computer Science ›› 2016, Vol. 43 ›› Issue (Z11): 486-489, 494.doi: 10.11896/j.issn.1002-137X.2016.11A.109

Previous Articles     Next Articles

Software Failure Prediction Model Based on Quasi-likelihood Method

ZHANG Xiao-feng and ZHANG De-ping   

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

Abstract: Software defect prediction is an important direction of software reliability research.Because there are many factors influencing the software failure,and the relationship among them is complicated,the joint distribution function is commonly used to describe the analysis model,which is difficult to be determined in the practical application.This problem may impact software defect prediction directly.In this paper,we proposed a Quasi-likelihood method (PCA-QLM),which uses PCA to select the main metrics firstly,and then build defect prediction model.In our model,we can use the mean function and variance function of dependent variable to get the estimated parameter and then predict defects.In this paper,we draw a conclusion that PCA-QLM can apply to the software failure prediction and its perfor-mance is better than other models by comparing with probit regression forecasting model and logistic regression forecasting model based on two real datasets Eclipse JDT and Eclipse PDE.

Key words: Software failure prediction,PCA,Logistic regression,Quasi-likelihood method

[1] Catal C.Performance Evaluation Metrics for Software FaultPrediction Studies[J].Acta Polytechnica Hungarica,2012,9(4):193-206
[2] 杨晓杏.基于度量元的软件缺陷预测技术[D].合肥:中国科学技术大学,2014:1-11
[3] Zhang F,Mockus A,Keivanloo I,et al.Towards building a universal defect prediction model[C]∥Working Conference on Mining Software Repositories.2014:182-191
[4] Suresh Y,Kumar L,Rath S K.Statistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite:A Comparative Analysis[J].International Scholarly Research Notices,2014:1-15
[5] Basili B V R,Briand L,Melo W L.A Validation of Object-O-riented Design Metrics As Quality Indicators[J].IEEE Transactions on Software Engineering,1996,2(10):751-756
[6] Briand L C,Wüst J,Daly J W,et al.Exploring the relationships between design measures and software quality in object-oriented systems[J].Journal of Systems & Software,2000,51(3):245-273
[7] Olague H M,Etzkorn L H,Gholston S,et al.Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes[J].IEEE Tran-sactions on Software Engineering,2007,33(6):402-419
[8] Kanmani S,Uthariaraj V R,Sankaranarayanan V,et al.Object oriented software quality prediction using general regression neural networks[J].Acm Sigsoft Software Engineering Notes,2004,29(5):1-6
[9] Nagappan N,Williams L,Vouk M,et al.Early estimation ofsoftware quality using in-process testing metrics:A controlled case study[J].Acm Sigsoft Software Engineering Notes,2005,30(4):1-7
[10] D’Ambros M,Lanza M,Robbes R.An extensive comparison of bug prediction approaches[C]∥IEEE Working Conference on Mining Software Repositories.2010:31-41
[11] Wedderburn R W M.Quasi-likelihood function,generalized linear models and the Gauss-Newton method[J].Biometrika.,1974,1(3):439-447
[12] Xia T,Jiang X,Wang X.Strong consistency of the maximumquasi-likelihood estimator in quasi-likelihood nonlinear models with stochastic regression[J].Statistics & Probability Letters,2015,103(4):391-400
[13] Su F,Chan K S.Quasi-likelihood estimation of a threshold diffusion process[J].Journal of Econometrics,2015,189(2):473-484
[14] Hu H C,Song L.Quasi-Maximum Likelihood Estimators inGeneralized Linear Models with Autoregressive Processes[J].数学学报(英文版),2014,30(12):2085-2102
[15] Liang K Y,Zeger S L.Logitudinal data analysis using generali-zed linear models[J].Biometrika,1986,73(1):13-22
[16] 陈夏,陈希孺.广义线性模型极大拟似然估计的强相合性和渐进正态性[J].应用概率统计,2005,1(3):251-263
[17] 张三国,廖源.关于广义线性模型拟似然估计弱相合性的几个问题[J].中国科学A辑:数学,2007,37(11):1369-1377
[18] 朱仲义.异方差非线性模型中的拟似然估计的渐进性质[J].河海大学学报,1999,24(2):110-115
[19] Ahmed S E,Fallahpour S.Shrinkage estimation strategy in quasi-likelihood models[J].Statistics & Probability Letters,2012,82(12):2170-2179
[20] 高启兵,吴耀华.广义线性回归拟似然估计的渐进正态性[J].系统科学与数学,2005,5(6):738-745

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 .