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All issues Volume 214 (2019) EPJ Web Conf., 214 (2019) 05007 References
Open Access
Issue
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
Article Number 05007
Number of page(s) 8
Section T5 - Software development
DOI https://doi.org/10.1051/epjconf/201921405007
Published online 17 September 2019
  1. T. Wenaus, J. Phys.: Conf. Ser. 898 1 (2017) [CrossRef] [Google Scholar]
  2. A. Kaur, I. Kaur, Journal of King Saud University - Computer and Information Sciences 30 2 (2016) [CrossRef] [Google Scholar]
  3. J. Han, M. Kamber, Data Mining Concepts and Techniques (Morgan Kauffman, 2006) [Google Scholar]
  4. Q. Taylor, C. Giraud-Carrier, International Journal of Data Analysis Techniques andStrategies 2 243, (2010) [CrossRef] [Google Scholar]
  5. N.S. Ali, V. Pawar, Int. J. Adv. Res. Comput. Sci. 4 172 (2013) [Google Scholar]
  6. H.K. Dam, T. Tran, A. Ghose, Explainable Software Analytics, in 40th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) (ACM, New York, NY, USA, 2018), pp. 53–56 [Google Scholar]
  7. V. Vapnik, The Nature of Statistical Learning Theory 2nd edn. (2000) [Google Scholar]
  8. C. Campbell, Y. Ying, Learning with Support Vector Machine (Morgan & ClaypoolPublishers, 2011) [Google Scholar]
  9. A. McCallum, K. Nigam, A comparison of Event Models for Naives Bayes Text Classification, in Learning for Text Categorization: Papers from the 1998 AAAI Workshop (1998) pp. 41–48, http://www.kamalnigam.com/papers/ multinomial-aaaiws98.pdf [Google Scholar]
  10. T.G. Dietterich, "Ensemble Learning" The Handbook of Brain Theory and Neural Networks (2002) Vol. 2 pp. 405–408, 2nd edn. [Google Scholar]
  11. Q. He, B. Shen, Y. Chen, Software Defect Prediction Using Semi-Supervised Learning with Change Burst Information, in IEEE 40th Annual Computer Software and Applica-tions Conference (COMPSAC) (2016) pp. 113–122 [Google Scholar]
  12. L. Breiman, Tech. rep. Department of Statistics, University of California, Berkley, California 94720 (1994) https://www.stat.berkeley.edu/~breiman/bagging.pdf [Google Scholar]
  13. L. Breiman, Mach. Learn. 45 5 (2001) [Google Scholar]
  14. Y. Freund, R.E. Schapire, J. Comput. Syst. Sci. 55 119 (1997) [Google Scholar]
  15. Y. Bengio, Found. Trends Mach. Learn. 2 1 (2009) [CrossRef] [Google Scholar]
  16. R. Raina, A. Battle, H. Lee, B. Packer, A.Y. Ng, Self-taught Learning: Transfer Learning from Unlabeled Data, in 24th international conference on Machine Learning (2007) pp. 759–766 [Google Scholar]
  17. R. Collobert, J. Weston, A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning, in 25th International Conference on Machine Learning (2008) [Google Scholar]
  18. T. McCabe, IEEE Trans. Softw. Eng. Se-2 308 (1976) [CrossRef] [Google Scholar]
  19. T.J. McCabe,, C. Butler, Artif. Intell. Language Process. 32 1415 (1989) [Google Scholar]
  20. V.Y. Shen, S.D. Conte, Tech. rep. Department of Computer Science - Purdue University (1981) [Google Scholar]
  21. M.H. Halstead, Elements of Software Science (Operating and programming systems series) (Elsevier Science Inc., New York, NY, USA, 1977) [Google Scholar]
  22. W. Li, S. Henry, Maintenance Metrics for the Object Oriented Paradigm, in First International Software Metrics Symposium (IEEE, 1993) [Google Scholar]
  23. S.R. Chidamber, C.F. Kemerer, IEEE Trans. Softw. Eng. 20 476 (1994) [CrossRef] [Google Scholar]
  24. C. Catal, B. Diri, Expert Syst. Appl. 36 7346 (2009) [Google Scholar]
  25. Y. LeCun, Y. Bengio, G. Hinton, Nature 521 436–444 (2015) [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  26. J. Deshmukh, K.M. Annervaz, S. Podder, Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques, in IEEE International Conference on Software Maintenance and Evolution (ICSME) (IEEE, 2017), pp. 115–124 [Google Scholar]
  27. M. Shepperd, Q. Song, Z. Sun, C. Mair, IEEE Trans. Softw. Eng. 39 1208 (2013) [CrossRef] [Google Scholar]
  28. Z. Zhang, X. Jing, T. Wang, Autom. Softw. Eng. 24 47 (2017) [CrossRef] [Google Scholar]
  29. Q. Song, Z. Jia, M. Shepperd, S. Ying, J. Liu, IEEE Trans. Softw. Eng. 37 356 (2011) [CrossRef] [Google Scholar]
  30. D. Gray, D. Bowes, N. Davey, The miuse of the NASA metrics data program data sets for automated software defect prediction, in 15th Annual Conference on Evaluation & Assessment in Software Engineering (EASE) (2011) [Google Scholar]
  31. Y. Zhou, H. Leung, IEEE Trans. Softw. Eng. 32 771 (2006) [CrossRef] [Google Scholar]
  32. S. Aleem, L.F. Capretz, F. Ahmed, Int. J. Softw. Eng. Knowl. Eng. 6 11 (2015) [Google Scholar]
  33. Nasa Defect Dataset, https://github.com/klainfo/NASADefectDataset [Google Scholar]
  34. Machine Learning & Data Mining Algorithms, http://tunedit.org/repo/PROMISE/DefectPrediction [Google Scholar]
  35. Bug prediction dataset, Evaluate your bug prediction approach on our benchmark, http://bug.inf.usi.ch [Google Scholar]
  36. Z. Tóth,, P. Gyimesi, R. Ferenc, Bug Database of GitHub Projects - A Public Bug Database of GitHub Projects and its Application in Bug Prediction, in Computational Science and Its Applications – ICCSA 2016. Lecture Notes in Computer Science edited by O. Gervasi et al. (Springer, Cham, 2016), Vol. 9789 http://www.inf.u-szeged.hu/~ferenc/papers/GitHubBugDataSet/ [Google Scholar]
  37. Weka 3: Data Mining Software in Java, https://www.cs.waikato.ac.nz/ml/weka/ [Google Scholar]
  38. scikit-learn, Machine Learning in Python, http://scikit-learn.org/stable/ [Google Scholar]
  39. The R Project for Statistical Computing, https://www.r-project.org/ [Google Scholar]

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