1 |
E. Hong, "Software Fault Prediction using Semi-supervised Learning Methods," Journal of the Institute of Internet, Broadcasting and Communication, Vol.19, No.3, pp.127-133, June 2019. DOI: https://doi.org/10.7236/JIIBC.2019.19.3.127
DOI
|
2 |
R. Malhotra, "A systematic review of machine learning techniques for software fault prediction," Applied Soft. Computing Vol.27, pp.504-518, 2015. DOI: https://doi.org/10.1016/j.asoc.2014.11.023
DOI
|
3 |
H. Lu, B. Cukic, and M. Culp, "A Semi-Supervised Approach to Software Defect Prediction," Proc. of COMPSAC, Sept. 2014. DOI: https://doi.org/10.1109/COMPSAC.2014.65
|
4 |
C. Catal and D. Banu. "Unlabelled extra data do not always mean extra performance for semi- supervised fault prediction," Expert Systems, Vol.26 No.5, pp.458-47, Nov. 2009. DOI:https://doi.org/10.1111/j.1468-0394.2009.00509.x
DOI
|
5 |
H. Lu, B. Cukic, and M. Culp, "An iterative semisupervised approach to software fault prediction," Proc. of PROMISE '11, 2011. DOI: https://doi.org/10.1145/2020390.2020405
|
6 |
Y. Jiang, M. Li, and Z.H. Zhou, "Software defect detection with ROCUS," Journal of Computer Science and Technology, Vol.26 No.2, pp.328-342. March 2011. DOI: https://doi.org/10.1007/s11390-011-9439-0
DOI
|
7 |
M. Li, H. Zhang, R. Wu, and Z. H. Zhou, "Samplebased software defect prediction with active and semi-supervised learning," Automated Software Engineering, Vol.19, No.2, pp.201-230, June 2012. DOI: https://doi.org/10.1007/s10515-011-0092-1
DOI
|
8 |
N. Seliya and T.M. Khoshgoftaar, "Software quality estimation with limited fault data: a semi- supervised learning perspective," Software Quality Journal Vol.15 No.3, pp.327-344, Sept. 2007. DOI: https://doi.org/10.1007/s11219-007-9013-8
DOI
|
9 |
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, "Collective classification in network data," AI magazine, Vol. 29, No.3, pp.93-106, 2008. DOI: https://doi.org/10.1609/aimag.v29i3.2157
|
10 |
E. Frank, M. A. Hall, and I. H. Witten, The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.
|
11 |
E. Hong, "Ambiguity Analysis of Defectiveness in NASA MDP data sets," Journal of Information Technology Services, Vol.12, No.2, pp.361-371, 2013. DOI: https://doi.org/10.9716/KITS.2013.12.2.361
DOI
|
12 |
M. Shepperd, Q. Song, Z. Sun, and C. Mair, "Data Quality : Some Comments on the NASA Software Defect Data Sets," IEEE Trans. Software Engineering, Vol.39, No.9, pp.1208-1215. Sept. 2013. DOI: https://doi.org/10.1109/TSE.2013.11
DOI
|
13 |
T. Fawcett, "An introduction to ROC analysis," Pattern recognition letters, Vol.27, No.8, pp.861- 874, June 2006. DOI: https://doi.org/10.1016/j.patrec.2005.10.010
DOI
|
14 |
Eun-Mi Kim, "Adaptive Network Model for the Recognition of Software Quality Attributes," Journal of KIIT, Vol.15, No.11, pp.103-109, 2017. DOI: https://10.14801/jkiit.2017.15.11.103
|