EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 245 (2020) EPJ Web Conf., 245 (2020) 05041 References
Open Access
Issue
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
Article Number 05041
Number of page(s) 8
Section 5 - Software Development
DOI https://doi.org/10.1051/epjconf/202024505041
Published online 16 November 2020
  1. S. Jha, R. Kumar, L.H. Son, M. Abdel-Basset, I. Priyadarshini, R. Sharma, H.V. Long, IEEE Access: special section on new trends in brain signal processing and analysis 7, 61840 (2019) [Google Scholar]
  2. N. Pritam, M. Khari, L.H. Son, R. Kumar, S. Jha, I. Priyadarshini, M. Abdel-Basset, H.V. Long, IEEE Access: New Trends in Brain Signal Processing and Analysis 7, 37414 (2019) [Google Scholar]
  3. L. Wei, W. Luo, J. Weng, Y. Zhong, X. Zhang, Z. Yan, IEEE Access: Internet-of-Things (IoT) Big Data Trust Management 5, 25591 (2017) [Google Scholar]
  4. F. Wu, X.Y. Jing, Y. Sun, L. Huang, F. Cui, Y. Sun, IEEE Transaction on Reliability 67, 581 (2018) [CrossRef] [Google Scholar]
  5. Z.W. Zhang, X.Y. Jing, F. Wu, IETSoftware 12, 527 (2018) [Google Scholar]
  6. J. Ge, J. Liu, W. Liu, Comparative Study on Defect Prediction Algorithms of Supervised Learning Software Based on Imbalanced Classification Data Sets, in 19th IEEE/ACIS International Conference on Softtware Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (IEEE, 2018) [Google Scholar]
  7. Meiliana, S. Karim, H.L.H.S. Warnars, F.L. Gaol, E. Abdurachman, B. Soewito, Software metrics for fault prediction using machine learning approaches: A literature review with PROMISE repository dataset, in IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) (IEEE, 2017) [Google Scholar]
  8. C. Catal, B. Diri, A Fault Prediction Model with Limited Fault Data to Improve Test Process, in Product-Focused Software Process Improvement, edited by A. Jedlitschka, O. Salo (Springer Berlin Heidelberg, Berlin, Heidelberg, 2008), pp. 244–257, ISBN 978-3-540-69566-0 [Google Scholar]
  9. X. Xuan, D. Lo, X. Xia, Y. Tian, Evaluating Defect Prediction Approaches Using a Massive Set of Metrics: An Empirical Study, in Proceedings of the 30th Annual ACM Symposium on Applied Computing (Association for Computing Machinery, New York, NY, USA, 2015), SAC ’15, pp. 1644–1647, ISBN 9781450331968, https://doi.org/10.1145/2695664.2695959 [Google Scholar]
  10. L. Goel, D. Damodaran, S.K. Khatri, M. Sharma, A literature review on cross project defect prediction, in 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON) (2017), pp. 680–685 [CrossRef] [Google Scholar]
  11. Shi Zhong, T.M. Khoshgoftaar, N. Seliya, Unsupervised learning for expert-based software quality estimation, in Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings. (2004), pp. 149–155 [CrossRef] [Google Scholar]
  12. A. Boucher, M. Badri, Using Software Metrics Thresholds to Predict Fault-Prone Classes in Object-Oriented Software, in 2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science Engineering (ACIT-CSII-BCD) (2016), pp. 169–176 [Google Scholar]
  13. J. Nam, S. Kim, CLAMI: Defect Prediction on Unlabeled Datasets, in 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) (IEEE, Lincoln, NE, USA, 2016) [Google Scholar]
  14. M. Yan, X. Zhang, C. Liu, L. Xu, M. Yang, D. Yang, Information and Software Technology 92, 1 (2017) [Google Scholar]
  15. D. Bonacorsi, T. Ferrari, Italian Meeting on High Energy Physics (2006) [Google Scholar]
  16. S. Huda, S. Alyahya, M. Mohsin Ali, S. Ahmad, J. Abawajy, H. Al-Dossari, J. Yearwood, IEEE Access 6, 2844 (2018) [Google Scholar]
  17. H. Zhang, X. Zhang, M. Gu, Predicting Defective Software Components from Code Complexity Measures, in 13th Pacific Rim International Symposium on Dependable Computing (PRDC 2007) (2007), pp. 93–96 [Google Scholar]
  18. S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand et al., Nucl. Instrum. Methods Phys. Res., Sect. A 506, 250 (2003) [CrossRef] [Google Scholar]
  19. Imagix, Imagix Source Code Analysis, https://www.imagix.com/products/source-code-analysis.html [Google Scholar]
  20. E. Ronchieri, M.G. Pia, T. Basaglia, M. Canaparo, Journal of Physics: Conf. Series 898 (2017) [Google Scholar]
  21. W. Rhmann, B. Pandey, G. Ansari, D.K. Pandey, Journal of King Saud UNiversity Computer and Information Sciences (2019) [Google Scholar]
  22. T. Menzies, J. Greenwald, A. Frank, IEEE Trans. Softw. Eng. 33, 2 (2007) [CrossRef] [Google Scholar]
  23. K. Herzig, S. Just, A. Rau, A. Zeller, Predicting defects using change genealogies, in 24th International Symposium on Software Reliability Engineering (ISSRE) (IEEE, 2013) [Google Scholar]
  24. M. D’Ambros, M. Lanza, R. Robbes, Empirical Software Engineering 17 (2012) [Google Scholar]
  25. Y.A. Alshehri, K. Goseva-Popstojanova, D.G. Dzielski, T. Devine, Applying Machine Learning to Predict Software Fault Proneness Using Change Metrics, Static Code Metrics, and a Combination of Them, in SoutheastCon (2018) [Google Scholar]
  26. Y. Gao, C. Yang, Software Defect Prediction based on Adaboost algorithm under Imbalance Distribution, in Proceedings of the 2016 4th International Conference on Sensors, Mechatronics and Automation ((2016)) [Google Scholar]
  27. J. Otero, L. Sànchez, Soft Computing 10 (2006) [Google Scholar]
  28. M.C.M. Prasad, L. Florence, A. Arya, Internation Journal of Database Theory and Applkcation 8 (2015) [Google Scholar]
  29. M.J. Siers, M.Z. Islam, Information Systems 51 (2015) [Google Scholar]
  30. N. Landwehr, M. Hall, E. Frank, Machine Learning 59 (2005) [Google Scholar]
  31. M. Gayathri, A. Sudha, International Journal of Recent Technology and Engineering (IJRTE) (2014) [Google Scholar]
  32. P.A. Selvaraj, D.P. Thangaraj, International Journal of Engineering & Technology Research 1 (2013) [Google Scholar]
  33. G. Luo, Y. Ma, K. Qin, IEICE Transactions on Information and Systems (2012) [Google Scholar]
  34. F. Wang, J. Huang, Y. Ma, A Top-k Learning to Rank Approach to Cross-Project Software Defect Prediction, in 25th Asia-Pacific Software Engineering Conference (2018) [Google Scholar]
  35. R.M. Magal, S.G. Jacob, International Journal of Computer Applications 117 (2015) [Google Scholar]
  36. J.R. Landis, G.G. Koch, Biometrics 33, 159 (1977) [Google Scholar]
  37. T. Fawcett, Pattern Recognition 28, 861 (2006) [Google Scholar]
  38. Y. Jiang, J. Lin, B. Cukic, T. Menzies, Variance analysis in software fault prediction models, in International Symposium in Software Reliability Engineering (2009), pp. 99–108 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.