Browse > Article
http://dx.doi.org/10.9708/jksci.2018.23.09.081

Defect Severity-based Defect Prediction Model using CL  

Lee, Na-Young (Dept. of Computer Science and Engnieering, Gangneung-Wonju National University)
Kwon, Ki-Tae (Dept. of Computer Science and Engnieering, Gangneung-Wonju National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.23, no.9, 2018 , pp. 81-86 More about this Journal
Abstract
Software defect severity is very important in projects with limited historical data or new projects. But general software defect prediction is very difficult to collect the label information of the training set and cross-project defect prediction must have a lot of data. In this paper, an unclassified data set with defect severity is clustered according to the distribution ratio. And defect severity-based prediction model is proposed by way of labeling. Proposed model is applied CLAMI in JM1, PC4 with the least ambiguity of defect severity-based NASA dataset. And it is evaluated the value of ACC compared to original data. In this study experiment result, proposed model is improved JM1 0.15 (15%), PC4 0.12(12%) than existing defect severity-based prediction models.
Keywords
Software Quality; Software Defect Severity; Label Clustering; Statistical Significance;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 E. S. Hong, M. K. Park, "Severity-based Software Quality Prediction using Class Imbalanced Data", Journal of the Korea Society of Computer and Information, Vol. 21, No. 4, pp. 77-80, 2016.
2 T. Menzies, J. Greenwald, and A. Frank, "Data mining static code attributes to learn defect predictors," IEEE Transaction on vol. 33, pp 2-13, 2007.
3 E. Engstrom, P. Runeson, and G. Wikstrand, "An empirical evaluation of regression testing based on fix-cache recommendations," in Software Testing, Verification and Validation (ICST), 2010 Third International Conference on, pp. 75-78, April 2010.
4 Jaechang Nam, Sunghun Kim, "CLAMI: Defect Prediction on Unlabeled Datasets", IEEE/ACM International Conference on Automated Software Engineering, 2015.
5 IEEE Computer Society, "IEEE Standard Classification for Software Anomalies", IEEE Std. 1044, 2009.
6 T. Menzies and A. Marcus, "Automated Severity Assessment of Software Defect Reports", Proc. of ICSM 2008, pp. 346-355, 2008.
7 Tracy Halla, Sarah Beechamb, David Bowesc, David Grayc, Steve Counsella, "A systematic literature review on fault prediction performance in software engineering," Software Engineering, IEEE Transactions on 38.6, pp. 1276-1304, 2012.   DOI
8 Y. Singh, A. Kaur and R. Malhotra, "Empirical validation of object-oriented metrics for predicting fault proneness models," Software Quality Journal, Vol.18, pp.3-35, 2010.   DOI
9 E. S. Hong, "Sortware Quality Prediction based on Defect Severity," Journal of the Korea Society of Computer and Information, Vol. 20, No. 5, pp. 73-81, 2015.   DOI
10 R. Malhotra, "A systematic review of machine learning techniques for software fault prediction.", Applied Soft Computing Vol.27, pp. 504-518, Feb. 2015.   DOI