F. Leo Johna,*, Jose Prabhu Joseph Johnb
|  Li L., Lessmann S., andBaesens B.Evaluating software defect prediction performance: an updated benchmarking study.arXiv preprint arXiv:1901.01726, 2019.
 Rathore, S.S. and Kumar, S.Nonlinear rule based ensemble methods for the prediction of number of faults. In
 Rhmann, W. and Ansari, G.A.Ensemble techniques-based software fault prediction in an open-source project. In
 Zhang Q., Pang G., andWang G.A novel sequential three-way decisions model based on penalty function.
 Eyoh I., John R., andDe Maere, G. Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction. In
 Xu Z., Liu J., Luo X., Yang Z., Zhang Y., Yuan P., Tang Y., andZhang T.Software defect prediction based on kernel PCA and weighted extreme learning machine.
 Halstead M.H.
 Tantithamthavorn C., Hassan A.E., andMatsumoto K.The impact of class rebalancing techniques on the performance and interpretation of defect prediction models.
 Lee T., Nam J., Han D., Kim S., andIn H.P.Developer micro interaction metrics for software defect prediction.
 Ghotra B.,McIntosh, S., and Hassan, A.E. Revisiting the impact of classification techniques on the performance of defect prediction models. In
 Rathore, S.S. and Kumar, S.Linear and non-linear heterogeneous ensemble methods to predict the number of faults in software systems.
 Malhotra, R. and Lata, K.An empirical study to investigate the impact of data resampling techniques on the performance of class maintainability prediction models.Neurocomputing, 2020.
 Wahono R.S., Herman N.S., andAhmad S.A comparison framework of classification models for software defect prediction.
 Li Z., Jing X.Y., Wu F., Zhu X., Xu B., andYing S.Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction.
 Geng W.Cognitive Deep Neural Networks prediction method for software fault tendency module based on Bound Particle Swarm Optimization.
 Tong H., Liu B., andWang S.Kernel spectral embedding transfer ensemble for heterogeneous defect prediction.IEEE Transactions on Software Engineering, 2019.
 Pascarella L., Palomba F., andBacchelli A.Fine-grained just-in-time defect prediction.
 Malhotra, R. and Sharma, A.Empirical assessment of feature selection techniques in defect prediction models using web applications.
 Kamei Y., Fukushima T., McIntosh, S., Yamashita, K., Ubayashi, N., and Hassan, A.E. Studying just-in-time defect prediction using cross-project models.
 Rahman, F. and Devanbu, P. How,why, process metrics are better. In
 Nagappan, N. and Ball, T.Use of relative code churn measures to predict system defect density. In
 Huang, F. and Bin, L.I.U. Software defect prevention based on human error theories.
 Swain, A.D. and Guttmann, H.E.
 Williams J.C.A data-based method for assessing and reducing human error to improve operational performance. In
 Hollnagel E.
 Shao Y., Liu B., Wang S., andLi G.Software defect prediction based on correlation weighted class association rule mining.
 Sun Z., Zhang J., Sun H., andZhu X.Collaborative filtering based recommendation of sampling methods for software defect prediction.
 N, Vijayaraj. and Ravi, T.N. Novel Clustered Bagging Model Using Cognitive Object Oriented Metrics for Cross-Project Software Defect Prediction,
 Jureczko, M. and Madeyski, L.Towards identifying software project clusters with regard to defect prediction. In
 Ferenc R., Tóth Z., Ladányi G., Siket I., andGyimóthy T.A public unified bug dataset for java. In
|No related articles found!|