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Bottlenecks in Software Defect Prediction Implementation in Industrial Projects Cover

Bottlenecks in Software Defect Prediction Implementation in Industrial Projects

Open Access
|Mar 2015

References

  1. [1] Catal, C. and Diri, B. (2009). A systematic review of software fault prediction studies., 36:7346-7354.
  2. [2] Fenton, N. and Neil, M. (1999). A critique of software defect prediction models., 25:675-689.
  3. [3] Hall, T., Beecham, S., Bowes, D., D., G., and Counsell, S. (2012). A systematic review of fault prediction performance in software engineering., 38:1276-1304.
  4. [4] Jureczko, M. and Madeyski, L. (2010). Towards identifying software project clusters with regard to defect prediction. In, PROMISE '10, pages 9:1-9:10, New York, NY, USA. ACM.
  5. [5] Khoshgoftaar, T., Allen, E., Hudepohl, J., and Aud, S. (1997). Application of neural networks to software quality modelling of a very large telecommunications system., 8:902-909.
  6. [6] Khoshgoftaar, T. and Seliya, N. (2004). Comparative assessment of software quality classification techniques: An empirical case study., 9:229-257.
  7. [7] Khoshgoftaar, T. and Seliya, N. (2005). Assessment of a new three-group software quality classification technique: An empirical case study., 10:183-218.
  8. [8] Klas, M., Nakao, H., Elberzhager, F., and Munch, J. (2008). Predicting defect content and quality assurance effectiveness by combining expert judgment and defect data-a case study., pages 17-26.
  9. [9] Li, P., Herbsleb, J., Shaw, M., and Robinson, B. (2006). Experiences and results from initiating field defect prediction and product test prioritization efforts at abb inc., pages 413-422.
  10. [10] Madeyski, L. and Jureczko, M. (2014). Which Process Metrics Can Significantly Improve Defect Prediction Models? An Empirical Study..
  11. [11] Madeyski, L. and Majchrzak, M. (2012). ImpressiveCode DePress (Defect Prediction for software systems) Extensible Framework. Available as an open source project from GitHub: https://github.com/ImpressiveCode/ic-depress.
  12. [12] Madeyski, L. and Majchrzak, M. (2014). Software Measurement and Defect Prediction with DePress Extensible Framework..
  13. [13] Ostrand, T. and Weyuker, E. (2002). The distribution of faults in a large industrial software system., 27:55-64.
  14. [14] Ostrand, T., Weyuker, E., and Bell, R. (2005). Predicting the location and number of faults in large software systems., 31:340-355.
  15. [15] Ostrand, T., Weyuker, E., and Bell, R. (2010). Programmer-based fault prediction., pages 1-10.
  16. [16] Rudiger, L., Lundberg, J., and Lowe, W. (2008). Comparing software metrics tools., pages 131-142.
  17. [17] Sliwierski, J., Zimmermann, T., and Zeller, A. (2005). When do changes induce fixes?.
  18. [18] Succi, G., Pedrycz, W., Stefanovic, M., and Miller, J. (2003). Practical assessment of the models for identification of defect-prone classes in object-oriented commercial systems using design metrics., 65:1-12.
  19. [19] Tosun, A., Bener, B., Turhan, B., and Menzies, T. (2010). Practical considerations in deploying statistical methods for defect prediction: A case study within the turkish telecommunications industry., 52:1242-1257.
  20. [20] Tosun, A., Turhan, B., and Bener, A. (2009). Practical considerations in deploying ai for defect prediction: A case study within the turkish telecommunication industry., page 11.
  21. [21] Turhan, B., Kocak, G., and Bener, A. (2009a). Data mining source code for locating software bugs: A case study in telecommunication industry., 36:9986-9990.
  22. [22] Turhan, B., Menzies, T., Bener, A., and Di Stefano, J. (2009b). On the relative value of cross-company and within-company data for defect prediction., 14:540-578.
  23. [23] Wong, W., Horgan, J., Syring, M., Zage, W., and Zage, D. (2000). Applying design metrics to predict fault-proneness: A case study on a large-scale software system., 30:1587-1608.
DOI: https://doi.org/10.1515/fcds-2015-0002 | Journal eISSN: 2300-3405 (formerly 0867-6356) | Journal ISSN: 0867-6356
Language: English
Page range: 17 - 33
Published on: Mar 1, 2015
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services

© 2015 Jarosław Hryszko, Lech Madeyski, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.