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  1. Published

    So You Need More Method Level Datasets for Your Software Defect Prediction? Voilà!

    Shippey, T., Hall, T., Counsell, S. & Bowes, D., 8/09/2016, ESEM '16 Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. New York: IEEE Computer Society, 6 p. 12

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  2. Published

    Automatically Identifying Code Features for Software Defect Prediction: Using AST N-grams

    Shippey, T., Bowes, D. & Hall, T., 02/2019, In: Information and Software Technology. 106, p. 142-160 19 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  3. Published

    Code cleaning for software defect prediction: A cautionary tale

    Shippey, T., Bowes, D., Counsell, S. & Hall, T., 29/08/2018, 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, p. 239-243 5 p.

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  4. Published

    Researcher bias: The use of machine learning in software defect prediction

    Shepperd, M., Bowes, D. & Hall, T., 1/06/2014, In: IEEE Transactions on Software Engineering. 40, 6, p. 603-616 14 p., 6824804.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  5. Published

    Authors' reply to 'comments on 'researcher bias: The use of machine learning in software defect prediction''

    Shepperd, M., Hall, T. & Bowes, D., 1/11/2018, In: IEEE Transactions on Software Engineering. 44, 11, p. 1129-1131 3 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  6. Published

    The Jinx on the NASA software defect data sets

    Petrić, J., Bowes, D., Hall, T., Christianson, B. & Baddoo, N., 1/06/2016, EASE '16 Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering. New York: Association for Computing Machinery, Inc, 5 p. 13

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  7. Published

    Building an Ensemble for Software Defect Prediction Based on Diversity Selection

    Petrić, J., Bowes, D., Hall, T., Christianson, B. & Baddoo, N., 8/09/2016, ESEM '16 Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. New York: Association for Computing Machinery, Inc, 10 p. 46

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  8. Published

    Using different characteristics of machine learners to identify different defect families

    Petric, J., 1/06/2016, EASE '16 Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering. New York: ACM, 4 p. 5

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  9. Published

    Software structure evolution and relation to system defectiveness

    Petric, J. & Grbac, T. G., 2014, EASE '14 Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering. New York: ACM, 34

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  10. Published

    How Effectively Is Defective Code Actually Tested? An Analysis of JUnit Tests in Seven Open Source Systems

    Petric, J., Hall, T. & Bowes, D., 10/10/2018, Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering. New York, NY, USA: ACM, p. 42-51 10 p. (PROMISE'18).

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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