Software change‐proneness prediction through combination of bagging and resampling methods
暂无分享,去创建一个
Lei Zhu | Long Cheng | Xiaoyan Zhu | Xiaolin Jia | Yueyang He | Xiaoyan Zhu | Yueyang He | Xiaolin Jia | Lei Zhu | Long Cheng
[1] Gary M. Weiss. Mining with rarity: a unifying framework , 2004, SKDD.
[2] Doo-Hwan Bae,et al. Measuring behavioral dependency for improving change-proneness prediction in UML-based design models , 2010, J. Syst. Softw..
[3] Carl G. Davis,et al. A Hierarchical Model for Object-Oriented Design Quality Assessment , 2002, IEEE Trans. Software Eng..
[4] Stan Matwin,et al. Feature Engineering for Text Classification , 1999, ICML.
[5] Shane McIntosh,et al. Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[6] Yann-Gaël Guéhéneuc,et al. An empirical study of the relationships between design pattern roles and class change proneness , 2008, 2008 IEEE International Conference on Software Maintenance.
[7] Mahmoud O. Elish,et al. A suite of metrics for quantifying historical changes to predict future change‐prone classes in object‐oriented software , 2013, J. Softw. Evol. Process..
[8] Anil K. Jain,et al. Classification of text documents , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).
[9] Tong-Seng Quah,et al. Application of neural networks for software quality prediction using object-oriented metrics , 2003, International Conference on Software Maintenance, 2003. ICSM 2003. Proceedings..
[10] Hongfang Liu,et al. Identifying and characterizing change-prone classes in two large-scale open-source products , 2007, J. Syst. Softw..
[11] Charles X. Ling,et al. AUC: A Better Measure than Accuracy in Comparing Learning Algorithms , 2003, Canadian Conference on AI.
[12] Andrea De Lucia,et al. Enhancing change prediction models using developer-related factors , 2018, J. Syst. Softw..
[13] Sallie M. Henry,et al. Object-oriented metrics that predict maintainability , 1993, J. Syst. Softw..
[14] Huan Liu,et al. Feature Selection for Classification , 1997, Intell. Data Anal..
[15] Seetha Hari,et al. Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.
[16] Taghi M. Khoshgoftaar,et al. Improving Software-Quality Predictions With Data Sampling and Boosting , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[17] Tracy Hall,et al. Software defect prediction: do different classifiers find the same defects? , 2017, Software Quality Journal.
[18] C. van Koten,et al. An application of Bayesian network for predicting object-oriented software maintainability , 2006, Inf. Softw. Technol..
[19] Geoff Holmes,et al. Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..
[20] Andrea De Lucia,et al. Developer-Related Factors in Change Prediction: An Empirical Assessment , 2017, 2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC).
[21] Yuming Zhou,et al. Examining the Potentially Confounding Effect of Class Size on the Associations between Object-Oriented Metrics and Change-Proneness , 2009, IEEE Transactions on Software Engineering.
[22] Rosa Maria Valdovinos,et al. The Imbalanced Training Sample Problem: Under or over Sampling? , 2004, SSPR/SPR.
[23] 郑肇葆,et al. 基于Naive Bayes Classifiers的航空影像纹理分类 , 2006 .
[24] Yuming Zhou,et al. Predicting object-oriented software maintainability using multivariate adaptive regression splines , 2007, J. Syst. Softw..
[25] Fred P. Brooks,et al. The Mythical Man-Month , 1975, Reliable Software.
[26] ZhangHongyu,et al. Comments on "Data Mining Static Code Attributes to Learn Defect Predictors" , 2007 .
[27] M. Stone. Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .
[28] Nong Ye,et al. Naïve Bayes Classifier , 2013 .
[29] Vandana Bhattacherjee,et al. Software Fault Prediction Using Quad Tree-Based K-Means Clustering Algorithm , 2012, IEEE Transactions on Knowledge and Data Engineering.
[30] Qinbao Song,et al. Using Coding-Based Ensemble Learning to Improve Software Defect Prediction , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[31] Enio G. Jelihovschi,et al. ScottKnott: A Package for Performing the Scott-Knott Clustering Algorithm in R , 2014 .
[32] David D. Lewis,et al. Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.
[33] Deepa Godara,et al. Understanding Change Prone Classes in Object Oriented Software , 2014 .
[34] Daniele Romano,et al. Using source code metrics to predict change-prone Java interfaces , 2011, 2011 27th IEEE International Conference on Software Maintenance (ICSM).
[35] Ruchika Malhotra,et al. A systematic review of machine learning techniques for software fault prediction , 2015, Appl. Soft Comput..
[36] Ruchika Malhotra,et al. An empirical study for software change prediction using imbalanced data , 2017, Empirical Software Engineering.
[37] Yi Zhang,et al. Classifying Software Changes: Clean or Buggy? , 2008, IEEE Transactions on Software Engineering.
[38] Sinan Eski,et al. An Empirical Study on Object-Oriented Metrics and Software Evolution in Order to Reduce Testing Costs by Predicting Change-Prone Classes , 2011, 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops.
[39] Adam Kowalczyk,et al. Second Order Features for Maximising Text Classification Performance , 2001, ECML.
[40] Thorsten Joachims,et al. Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.
[41] Mohammad Alshayeb,et al. An Empirical Validation of Object-Oriented Metrics in Two Different Iterative Software Processes , 2003, IEEE Trans. Software Eng..
[42] Xin Yao,et al. Using Class Imbalance Learning for Software Defect Prediction , 2013, IEEE Transactions on Reliability.
[43] James M. Bieman,et al. Design patterns and change proneness: an examination of five evolving systems , 2003, Proceedings. 5th International Workshop on Enterprise Networking and Computing in Healthcare Industry (IEEE Cat. No.03EX717).
[44] Taghi M. Khoshgoftaar,et al. Experimental perspectives on learning from imbalanced data , 2007, ICML '07.
[45] Chris F. Kemerer,et al. Towards a metrics suite for object oriented design , 2017, OOPSLA '91.
[46] Tracy Hall,et al. A Systematic Literature Review on Fault Prediction Performance in Software Engineering , 2012, IEEE Transactions on Software Engineering.
[47] Mark A. Hall,et al. Correlation-based Feature Selection for Machine Learning , 2003 .
[48] Yuming Zhou,et al. The ability of object-oriented metrics to predict change-proneness: a meta-analysis , 2011, Empirical Software Engineering.
[49] Nello Cristianini,et al. Controlling the Sensitivity of Support Vector Machines , 1999 .
[50] Elliot Soloway,et al. Where the bugs are , 1985, CHI '85.
[51] Ron Kohavi,et al. Irrelevant Features and the Subset Selection Problem , 1994, ICML.
[52] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[53] Wei Li,et al. Object-Oriented Metrics Which Predict Maintainability , 1993 .
[54] M. Braga,et al. Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..
[55] Qinbao Song,et al. A Comprehensive Investigation of the Role of Imbalanced Learning for Software Defect Prediction , 2019, IEEE Transactions on Software Engineering.