Online Defect Prediction for Imbalanced Data

By Ming Tan, Lin Tan, Sashank Dara, Caleb Mayeux. 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, 2015

301 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/b7c813672d559e3fe37569383d616cf3175af617

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Chakkrit Kla Tantithamthavorn
2. Ahmed E. Hassan
3. Ken-ichi Matsumoto
4. Michael R. Lyu
5. Shane McIntosh
6. Yangruibo Ding
7. Lin Tan
8. Jieming Zhu

In Information Systems

1. Chakkrit Kla Tantithamthavorn
2. Ken-ichi Matsumoto
3. Ahmed E. Hassan
4. Shane McIntosh
5. Michael R. Lyu
6. Jian Li
7. Pinjia He
8. Jieming Zhu

In Software

1. Chakkrit Kla Tantithamthavorn
2. Ken-ichi Matsumoto
3. Ahmed E. Hassan
4. Shane McIntosh
5. Michael R. Lyu
6. Jian Li
7. Pinjia He
8. Jieming Zhu

Citations in the corpus

• Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset (2018, unknown): “In information technology, software defect detection [24] and network intrusion detection [25] are implemented under imbalanced scenarios.”
• Software Defect Prediction via Convolutional Neural Network (2017, background): “Software defect prediction is an active research area in Software Engineering [3], [4], [5], [6], [7], [13], [27], [28].”
• An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017, unknown): “5) [1], [5], [62], [86], [95], and (2) are sensitive to imbalanced data [23], [44], [64], [67], [103].”
• CoCoNuT: combining context-aware neural translation models using ensemble for program repair (2020, methodology): “This setup is to ensure the validity of experiments so that no future data is used [75] (Section 5).”
• The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, methodology): “[21] investigate the impact of class rebalancing techniques on 7 commercial and opensource datasets using 7 classification techniques (i.”
• The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, background): “because they: (1) depend on an arbitrarily-selected threshold [1], [5], [80], [113], [124], [136] and (2) are sensitive to imbalanced data [24], [50], [84], [92], [132], [136].”
• Deep Learning Based Vulnerability Detection: Are We There Yet? (2020, background): “SMOTE has shown to be effective in a number of domains with imbalanced datasets [37], [38].”