Defect Prediction

  1. Are Fix-Inducing Changes a Moving Target? A Longitudinal Case Study of Just-In-Time Defect Prediction
    Authors - Shane McIntosh, Yasutaka Kamei
    Venue - IEEE Transactions on Software Engineering, pp. To appear, 2017
    Preprint - PDF
    Related Tags - TSE 2017 software quality defect prediction
  2. A Framework for Evaluating the Results of the SZZ Approach for Identifying Bug-Introducing Changes
    Authors - Daniel Alencar da Costa, Shane McIntosh, Weiyi Shang, Uirá Kulesza, Roberta Coelho, Ahmed E. Hassan
    Venue - IEEE Transactions on Software Engineering, Vol. 43, No. 7, pp. 641–657, 2017
    Preprint - PDF
    Related Tags - TSE 2017 software quality defect prediction
  3. The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction Models
    Authors - Feng Zhang, Ahmed E. Hassan, Shane McIntosh, Ying Zou
    Venue - IEEE Transactions on Software Engineering, Vol. 43, No. 5, pp. 476-491, 2017
    Preprint - PDF
    Related Tags - TSE 2017 software quality defect prediction
  4. An Empirical Comparison of Model Validation Techniques for Defect Prediction Models
    Authors - Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto
    Venue - IEEE Transactions on Software Engineering, Vol. 41, No. 1, pp. 1-18, 2017
    Preprint - PDF
    Related Tags - TSE 2017 software quality defect prediction
  5. Automated Parameter Optimization of Classification Techniques for Defect Prediction Models
    Authors - Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto
    Venue - International Conference on Software Engineering, pp. 321-332, 2016
    Acceptance rate - 101/530 (19%)
    Preprint - PDF
    Related Tags - ICSE 2016 software quality defect prediction
  6. The Relationship between Commit Message Detail and Defect Proneness in Java Projects on GitHub
     Mining challenge runner-up 
    Authors - Jacob G. Barnett, Charles K. Gathuru, Luke S. Soldano, Shane McIntosh
    Venue - International Conference on Mining Software Repositories, Mining challenge, pp. 496-499, 2016
    Acceptance rate - 10/24 (42%)
    Preprint - PDF
    Related Tags - MSR 2016 software quality defect prediction
  7. Comments on "Researcher Bias: The Use of Machine Learning in Software Defect Prediction"
    Authors - Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto
    Venue - IEEE Transactions on Software Engineering, Vol. 42, No. 11, pp. 1092-1094, 2016
    Preprint - PDF
    Related Tags - TSE 2016 software quality defect prediction
  8. Studying just-in-time defect prediction using cross-project models
    Authors - Yasutaka Kamei, Takafumi Fukushima, Shane McIntosh, Kazuhiro Yamashita, Naoyasu Ubayashi, Ahmed E. Hassan
    Venue - Empirical Software Engineering, Vol. 21, No. 5, pp. 2072-2106, 2016
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    Related Tags - EMSE 2016 software quality defect prediction
  9. The Impact of Mislabelling on the Performance and Interpretation of Defect Prediction Models
    Authors - Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Akinori Ihara, Kenichi Matsumoto
    Venue - International Conference on Software Engineering, pp. 812-823, 2015
    Acceptance rate - 84/452 (19%)
    Preprint - PDF
    Related Tags - ICSE 2015 software quality defect prediction
  10. Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models
    Authors - Baljinder Ghotra, Shane McIntosh, Ahmed E. Hassan
    Venue - International Conference on Software Engineering, pp. 789-800, 2015
    Acceptance rate - 84/452 (19%)
    Preprint - PDF
    Related Tags - ICSE 2015 software quality defect prediction
  11. An Empirical Study of Just-In-Time Defect Prediction Using Cross-Project Models
     Invited for journal extension 
    Authors - Takafumi Fukushima, Yasutaka Kamei, Shane McIntosh, Kazuhiro Yamashita, Naoyasu Ubayashi
    Venue - Working Conference on Mining Software Repositories, pp. 172-181, 2014
    Acceptance rate - 29/85 (34%)
    Preprint - PDF
    Related Tags - MSR 2014 software quality defect prediction