Computer Science ›› 2016, Vol. 43 ›› Issue (7): 186-190.doi: 10.11896/j.issn.1002-137X.2016.07.034
Previous Articles Next Articles
XIONG Jing, GAO Yan and WANG Ya-yu
| [1] Wang Qing,Wu Shu-jian,Li Ming-shu.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] Khoshgoftaar T M,Seliya N.Tree-based software quality estimation models for fault prediction[C]∥IEEE Symposium on Metrics.2002:203-214 [3] Khoshgoftaar T M,Allen E B,Jones W D,et al.Accuracy of software quality models over multiple releases[J].Annals of Software Engineering,2000,9(1/2):103-116 [4] Khoshgoftaar T M,Liu Y,Seliya N.A multi-objective module-order model for software quality enhancement[J].IEEE Tran-sactions on Evolutionary Computation,2004,8(6):593-608 [5] Rumelhart D E,Hinton G E,Williams R J.Learning representations by back-propagating errors[J].Nature,2010,2(6088):533-536 [6] Gupta N,Singh M P.Estimation of software reliability with execution time model using the pattern mapping technique of artificial neural network[J].Computers & Operations Research,2005,32(1):187-199 [7] Khoshgoftaar T M,Pandya A S.A neural-network approach for predicting software development faults[J].IEEE Computer,1992,17(8):83-88 [8] Lessmann S,Baesens B,Mues C,et al.Benchmarking classification models for software defect prediction:A proposed framework and novel findings[J].IEEE Transactions on Software Engineering,2008,34(4):485-496 [9] Ghotra B,McIntosh S,Hassan A E.Revisiting the impact of classification techniques on the performance of defect prediction models[C]∥Proceedings of the International Conference on Software Engineering.2015 [10] Wang S,Yao X.Using class imbalance learning for software defect prediction[J].IEEE Transactions on Reliability,2013,62(2):434-443 [11] Jing X Y,Ying S,Zhang Z W,et al.Dictionary learning based software defect prediction[C]∥Proceedings of the International Conference on Software Engineering.2014:414-423 [12] Seiffert C,Khoshgoftaar T M,Van Hulse J,et al.An empirical study of the classification performance of learners on imbalanced and noisy software quality data[J].Information Sciences,2014,259:571-595 [13] Liu M X,Miao L S,Zhang D Q.Two-stage cost-sensitive lear-ning for software defect prediction[J].IEEE Transactions on Reliability,2014,63(2):676-686 [14] He Yong,Xiang Li-guo.Study and Application of BP NeuralNetwork Model Based on Fuzzy Clustering[J].Systems Engineering-theory & Practice,2004,24(2):79-82(in Chinese) 何勇,项利国.基于模糊聚类的 BP 神经网络模型研究及应用[J].系统工程理论与实践,2004,24(2):79-82 [15] Thwin M M T,Quah T S.Application of neural networks for software quality prediction using object-oriented metrics[J].Journal of Systems and Software,2005,76(2):147-156 [16] Liu Tian-shu.The Research and Application on BP Neural Network Improvement[D]Harbin:Northeast Forestry University,2011(in Chinese) 刘天舒.BP神经网络的改进研究及应用[D].哈尔滨:东北林业大学,2011 [17] Ma S,Du T.Improved Adaboost face detection[C]∥International Conference on Measuring Technology and Mechatronics Automation (ICMTMA).2010:434-437 [18] Zhang H,Xie Y,Xu C.A classifier training method for face detection based on Adaboost[C]∥International Conference on Transportation,Mechanical,and Electrical Engineering (TMEE).2011:731-734 [19] Freund Y,Schapire R E.A decision-theoretic generalization ofon-line learning and an application to boosting[C]∥Computational learning theory.Berlin Heidelberg,1995:23-37 [20] Zhang Z,Xie X.Research on Adaboost M1 with random forest[C]∥International Conference on Computer Engineering and Technology (ICCET).2010:647-652 [21] NASA.The NASA Metrics Data Program.http://mdp.ivv.nasa.gov |
| No related articles found! |
|
||
Home