클러스터링라벨(CL)을 이용한 결함심각도 기반 결함예측 모델
Defect Severity-based Defect Prediction Model using CL
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초록
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.
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.
참고문헌 (10)
- T. Menzies, "Data mining static code attributes to learn defect predictors", IEEE Transaction on, 33, pp.2~13 (2007)
- E. Engstrom, "An empirical evaluation of regression testing based on fix-cache recommendations", Software Testing, Verification and Validation (ICST), 2010 Third International Conference on, pp.75~78 (2010)
- Jaechang Nam, "CLAMI: Defect Prediction on Unlabeled Datasets", IEEE, 2015, ~
- IEEE Computer Society, "IEEE Standard Classification for Software Anomalies, IEEE Std. 1044", ~ (2009)
- T. Menzies, "Automated Severity Assessment of Software Defect Reports", Proc. of ICSM 2008, pp.346~355 (2008)
- T. Hall, "A Systematic Literature Review on Fault Prediction Performance in Software Engineering", IEEE Transactions on Software Engineering, 38(6), pp.1276~1304 (2012), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Yogesh Singh, "Empirical validation of object-oriented metrics for predicting fault proneness models", Software Quality Journal, 18(1), pp.3~35 (2010), Springer Nature[DOI]
- 홍의석, "결함 심각도에 기반한 소프트웨어 품질 예측", 한국컴퓨터정보학회논문지, 20(5), pp.73~81 (2015), 한국컴퓨터정보학회
- Ruchika Malhotra, "A systematic review of machine learning techniques for software fault prediction", Applied Soft Computing, 27, pp.504~518 (2015), Elsevier BV[DOI]
- 홍의석, "클래스 불균형 데이터를 사용한 심각도 기반 소프트웨어 품질 예측", 한국컴퓨터정보학회논문지, 21(4), pp.73~80 (2016), 한국컴퓨터정보학회