Cross-project defect prediction using data sampling for class imbalance learning: an empirical study

The presence of defect data related to different projects leads to cross-project defect prediction an open issue in the field of research in software engineering. In cross-project defect prediction...

[1]  Lionel C. Briand,et al.  A Comprehensive Investigation of Quality Factors in Object-Oriented Designs: an Industrial Case Study , 1998 .

[2]  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).

[3]  Jens Grabowski,et al.  A Comparative Study to Benchmark Cross-Project Defect Prediction Approaches , 2018, IEEE Transactions on Software Engineering.

[4]  Jongmoon Baik,et al.  A transfer cost-sensitive boosting approach for cross-project defect prediction , 2017, Software Quality Journal.

[5]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[6]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[7]  Zhi-Hua Zhou,et al.  Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .

[8]  Daoqiang Zhang,et al.  Two-Stage Cost-Sensitive Learning for Software Defect Prediction , 2014, IEEE Transactions on Reliability.

[9]  Guangchun Luo,et al.  Transfer learning for cross-company software defect prediction , 2012, Inf. Softw. Technol..

[10]  Baowen Xu,et al.  Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning , 2015, ESEC/SIGSOFT FSE.

[11]  Steffen Herbold,et al.  Training data selection for cross-project defect prediction , 2013, PROMISE.

[12]  Jin Liu,et al.  Dictionary learning based software defect prediction , 2014, ICSE.

[13]  Andrea De Lucia,et al.  Cross-project defect prediction models: L'Union fait la force , 2014, 2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE).

[14]  Xin Yao,et al.  Using Class Imbalance Learning for Software Defect Prediction , 2013, IEEE Transactions on Reliability.

[15]  Mei-Hwa Chen,et al.  An empirical study on object-oriented metrics , 1999, Proceedings Sixth International Software Metrics Symposium (Cat. No.PR00403).

[16]  Chris F. Kemerer,et al.  A Metrics Suite for Object Oriented Design , 2015, IEEE Trans. Software Eng..

[17]  Harald C. Gall,et al.  Cross-project defect prediction: a large scale experiment on data vs. domain vs. process , 2009, ESEC/SIGSOFT FSE.

[18]  Karim O. Elish,et al.  Predicting defect-prone software modules using support vector machines , 2008, J. Syst. Softw..

[19]  Gerardo Canfora,et al.  Multi-objective Cross-Project Defect Prediction , 2013, 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation.

[20]  Josef Kittler,et al.  Inverse random under sampling for class imbalance problem and its application to multi-label classification , 2012, Pattern Recognit..

[21]  Qiang Yang,et al.  Boosting for transfer learning , 2007, ICML '07.

[22]  Ahmet Zengin,et al.  HSDD: A hybrid sampling strategy for class imbalance in defect prediction data sets , 2016, 2016 Eleventh International Conference on Digital Information Management (ICDIM).

[23]  Tim Menzies,et al.  Better cross company defect prediction , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).

[24]  Jongmoon Baik,et al.  Value-cognitive boosting with a support vector machine for cross-project defect prediction , 2014, Empirical Software Engineering.

[25]  Jun Zheng,et al.  Cost-sensitive boosting neural networks for software defect prediction , 2010, Expert Syst. Appl..

[26]  Huanhuan Chen,et al.  Negative correlation learning for classification ensembles , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[27]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[28]  Taeho Jo,et al.  A Multiple Resampling Method for Learning from Imbalanced Data Sets , 2004, Comput. Intell..

[29]  Zhaowei Shang,et al.  Negative samples reduction in cross-company software defects prediction , 2015, Inf. Softw. Technol..

[30]  Ayse Basar Bener,et al.  Empirical evaluation of the effects of mixed project data on learning defect predictors , 2013, Inf. Softw. Technol..

[31]  Ying Zou,et al.  Cross-Project Defect Prediction Using a Connectivity-Based Unsupervised Classifier , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).

[32]  Tim Menzies,et al.  Heterogeneous Defect Prediction , 2015, IEEE Transactions on Software Engineering.

[33]  Osamu Mizuno,et al.  A Cross-Project Evaluation of Text-Based Fault-Prone Module Prediction , 2014, 2014 6th International Workshop on Empirical Software Engineering in Practice.

[34]  Bart Baesens,et al.  Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers , 2013, IEEE Transactions on Software Engineering.

[35]  Lionel C. Briand,et al.  Exploring the relationships between design measures and software quality in object-oriented systems , 2000, J. Syst. Softw..

[36]  Alois Knoll,et al.  Gradient boosting machines, a tutorial , 2013, Front. Neurorobot..

[37]  Sinno Jialin Pan,et al.  Transfer defect learning , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[38]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[39]  Rongxin Wu,et al.  Dealing with noise in defect prediction , 2011, 2011 33rd International Conference on Software Engineering (ICSE).

[40]  David Lo,et al.  HYDRA: Massively Compositional Model for Cross-Project Defect Prediction , 2016, IEEE Transactions on Software Engineering.

[41]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.