Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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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)
  • Received : 2018.07.31
  • Accepted : 2018.08.26
  • Published : 2018.09.28
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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.

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References

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