Open Access
Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
---|---|---|
Article Number | 01060 | |
Number of page(s) | 9 | |
Section | Network Security System, Neural Network and Data Information | |
DOI | https://doi.org/10.1051/matecconf/201823201060 | |
Published online | 19 November 2018 |
- G. J. Pai and J. B. Dugan, “Empirical analysis of software fault content and fault proneness using Bayesian methods,” IEEE Trans. Softw. Eng., 33(10), pp. 675-686, (2007) [CrossRef] [Google Scholar]
- C. S. Wright and T. A. Zia, “A quantitative analysis into the economics of correcting software bugs,” in Proc. Int. Conf. Comput. Intell. Security Inf. Syst., Torremolinos, (2011) [Google Scholar]
- W. E. Wong, V. Debroy, A. Surampudi, H. Kim, and M. F. Siok, “Recent catastrophic accidents: Investigating how software was responsible,” in Proc. 4th Int. Conf. Secure Softw. Integr. Rel.Improvement, (2010) [Google Scholar]
- I. Vessey, “Expertise in debugging computer programs: A process analysis,” International Journal of Man-Machine Studies, 23(5):459-494,(1985). [CrossRef] [Google Scholar]
- T. Wang, “Post-mortem dynamic analysis for software debugging,” Ph.D dissertation, FudanUniv. (2007) [Google Scholar]
- W. E. Wong, R. Gao, Y. Li, R. Abreu, & F. Wotawa, “A Survey on Software Fault Localization,” IEEE Trans. Softw. Eng.,42, 707-740(2016) [CrossRef] [Google Scholar]
- X. Chen, X. Ju, W. Wen, Q. Gu, “Review of dynamic fault localization approaches based on program spectrum,” Journal of Software, 26(2),(2015) [Google Scholar]
- J. A. Jones, J. Bowring, and M. J. Harrold, “Debugging in parallel,” in Proc. ACM/SIGSOFT Int. Symp. Softw. Testing Anal., (2007) [Google Scholar]
- R. Abreu, A. Gonz_alez, and A. J. Gemund, “Exploiting count spectra for Bayesian fault localization,” presented at the 6th Int.Conf. Predictive Models Softw. Eng., (2010) [Google Scholar]
- L. C. Briand, Y. Labiche, and X. Liu, “Using machine learning to support debugging with tarantula,” in Proc. IEEE Int. Symp. Softw. Rel., (2007) [Google Scholar]
- J. A. Jones, and M. J. Harrold, “Empirical evaluation of the tarantula automatic fault-localization technique,” in Proc. Int. Conf. Autom. Softw. Eng., (2005) [Google Scholar]
- W. E. Wong, T. Sugeta, Y. Qi, and J. C. Maldonado, “Smart debugging software architectural design in SDL,” J. Syst. Softw., 76(1), pp. 15–28, (2005) [CrossRef] [Google Scholar]
- L. C. Ascari, L. Y. Araki, A. R. T. Pozo, and S. R. Vergilio, “Exploring machine learning techniques for fault localization,” in Proc. 10th Latin Am. Test Workshop, (2009) [Google Scholar]
- W. E. Wong and Y. Qi, “BP neural network-based effective fault localization,” Int. J. Softw. Eng. Knowl. Eng., 19(4), pp. 573–597, (2009) [CrossRef] [Google Scholar]
- W. E. Wong, V. Debroy, R. Golden, X. Xu, and B. Thuraisingham, “Effective software fault localization using an RBF neural network,” IEEE Trans. Rel., 61(1), pp. 149-169, (2012) [CrossRef] [Google Scholar]
- C. C. Lee, P. C. Chung, J. R. Tsai, and C. I. Chang, “Robust radial basis function neural networks,” IEEE Trans. Syst., Man, Cybern. B, Cybern., 29(6), pp. 674-685, (1999) [Google Scholar]
- P. D. Wasserman, “Advanced Methods in Neural Computing,” (1993) [Google Scholar]
- J. L. He, H. Zhang, “Application of Artificial Neural Network in Software Multiple faults Location,” Jorunal of Computer Research and Development, 50(3),(2013) [Google Scholar]
- T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning: Data Mining”, Inference and Prediction. Springer,( 2002) [Google Scholar]
- T. M. Khoshgoftaar, E. B. Allen, J. P. Hudepohl, and S. J. Aud, “Application of Neural Networks to Software Quality Modeling of a Very Large Telecommunications System,” IEEE Trans. Neural Networks, 8(4) pp. 902-909, (1997) [CrossRef] [Google Scholar]
- T. V. Gestel, J. A. K. Suykens, B. Baesens, S. Viaene, J. Vanthienen, G. Dedene, B. De Moor, and J. Vandewalle, “Benchmarking Least Squares Support Vector Machine Classifiers,” Machine Learning, 54(1) pp. 5-32, (2004) [CrossRef] [Google Scholar]
- T. M. Khoshgoftaar and N. Seliya, “Analogy-Based Practical Classification Rules for Software Quality Estimation,” Empirical Software Eng., 8(4), pp. 325350, (2003) [CrossRef] [Google Scholar]
- R. W. Selby and A. A. Porter, “Learning from Examples: Generation and Evaluation of Decision Trees for Software Resource Analysis, “ IEEE Trans. Software Eng., 14(12) pp. 1743-1756, (1988) [CrossRef] [Google Scholar]
- L. Breiman, “Random Forests,"Machine Learning, 45(1), (2001) [Google Scholar]
- H. Do, S. Elbaum, G. Rothermel, “Supporting controlled experimentation with testing techniques: An infrastructure and its potential impact,” Empirical Software Engineering,10(4),(2005) [Google Scholar]
- Y. Lei, X. G. Mao, Z. Y. Dai, C. S. Wang, “Effective statistical fault localization using program slices,” 2012 IEEE 36th Inter. Conf. Comp. Softw. and Appl, (2012) [Google Scholar]
- V. Debroy, W. E. Wong, X. Xu and B. Choi, “A Grouping-Based Strategy to Improve the Effectiveness of Fault Localization Techniques,” 10th Inter. Conf. Qual. Softw., (2010) [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.