Conditional Domain Adversarial Adaptation for Heterogeneous Defect Prediction

Abstract

This paper would not have been possible without the generous support by joint Ph.D. program of "double first rate" construction disciplines of CUMT.

Improved SMOTE and Optimized Siamese Neural Networks for Class Imbalanced Heterogeneous Cross Project Defect Prediction

“…The performance metrics namely accuracy, precision, recall, f-measure, specificity and processing time are evaluated. The state-of-the-art methods of BRFSS [9], CTKCCA [12], KSETE [14], CDAA [15], FSLBDA [17], MSTL-AE [18] and FTLKD-CNN [19] are also implemented in the same simulation setting to compare their performance with the proposed method denoted as GEO-SNN. the other methods with high values of accuracy, precision, recall, f-measure, and specificity and less processing time.…”

“…the other methods with high values of accuracy, precision, recall, f-measure, and specificity and less processing time. The GEO-SNN has approximately 3%, 6%, 3%, 3%, 8%, 6% and 4% high accuracy than BRFSS [9], CTKCCA [12], KSETE [14], CDAA [15], FSLBDA [17], MSTL-AE [18] and FTLKD-CNN [19], respectively. The use of effective class imbalance processing and deep feature learning with parameter tuned SNN has been the major reason for this improvement.…”

“…The results in Table 4 show that the proposed GEO-SNN method has conveniently outperformed the other methods. GEO-SNN has approximately 3%, 8%, 4%, 4%, 10%, 6% and 5% high accuracy than BRFSS [9], CTKCCA [12], KSETE [14], CDAA [15], FSLBDA [17], MSTL-AE [18] and FTLKD-CNN [19], respectively. The proposed GEO-SNN has provided better defect prediction with heterogeneous metrics very effectively.…”

“…Heterogeneous Cross-Project Defect Prediction (HCPDP) or simply Heterogeneous Defect Prediction (HDP) [7] is the heterogeneous type of SDP based on the principle of CPDP with the additional characteristic of independent nature on whether the International Journal of Intelligent Engineering and Systems, Vol. 15 source and target projects have common metrics or not. Different metrics based multiple source projects is still challenging for HCPDP.…”

Heterogeneous Defect Prediction Based on Federated Transfer Learning via Knowledge Distillation

“…A sparse representation based double obfuscation algorithm is designed and applied to HDP [7]. Gong et al proposed a novel conditional domain adversarial adaptation (CDAA) approach to tackle heterogeneous problem, which is motivated by generative adversarial network (GAN) [8]. Through lots of experiments, Chen et al find multiple projects cannot improve the performance of HDP approaches consistently, since the label information in the target project is not used effectively [9].…”