Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction

Li, Zhiqiang; Jing, Xiao‐Yuan; Wu, Fei; Zhu, Xiaoke; Xu, Bin; Song, Ying

doi:10.1007/s10515-017-0220-7

Cited by 99 publications

(80 citation statements)

References 90 publications

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“…In this section, we compare with DNN (DNN utilizes crossentropy loss as the classification error and has the same network architecture with SNN), CNN (CNN applies the approach proposed by [34] to balance data before training neural network), some HDP methods including CCA+ [13] and CTKCA [43], some CPDP methods including NN-Filter [9], VCB-SVM [11] and TCBoost [12], and WPDP approach to validate the performance of our approach for HDP. The results of Recall, AUC and MCC are listed in Tables 6, 7 and 8.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…In order to address RQ2, we compare our SNN with DNN (DNN utilizes cross-entropy loss as the classification error and has the same network architecture with SNN), CNN (CNN applies the approach proposed by [34] to balance data before training neural network) and two exiting HDP approaches including CCA+ [13], and CTKCCA [43]. The settings of these approaches are the same as our approach.…”

Section: Rq2: Could Snn Approach Obtain Better Performance For Hdp?mentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised Deep Domain Adaptation for Heterogeneous Defect Prediction

Gong

Jiang

et al. 2019

IEICE Trans. Inf. & Syst.

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Heterogeneous defect prediction (HDP) is to detect the largest number of defective software modules in one project by using historical data collected from other projects with different metrics. However, these data can not be directly used because of different metrics set among projects. Meanwhile, software data have more non-defective instances than defective instances which may cause a significant bias towards defective instances. To completely solve these two restrictions, we propose unsupervised deep domain adaptation approach to build a HDP model. Specifically, we firstly map the data of source and target projects into a unified metric representation (UMR). Then, we design a simple neural network (SNN) model to deal with the heterogeneous and class-imbalanced problems in software defect prediction (SDP). In particular, our model introduces the Maximum Mean Discrepancy (MMD) as the distance between the source and target data to reduce the distribution mismatch, and use the cross-entropy loss function as the classification loss. Extensive experiments on 18 public projects from four datasets indicate that the proposed approach can build an effective prediction model for heterogeneous defect prediction (HDP) and outperforms the related competing approaches.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Rq2: Could Snn Approach Obtain Better Performance For Hdp?mentioning

confidence: 99%

Unsupervised Deep Domain Adaptation for Heterogeneous Defect Prediction

Gong

Jiang

et al. 2019

IEICE Trans. Inf. & Syst.

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“…Class imbalance problem in software defect prediction is addressed by various methods based on data and algorithm levels [15,16]. Data-level methods address the issue by means of re-sampling techniques, which may balance datasets by deleting majority class data samples or by replicating minority class samples.…”

Section: Related Workmentioning

confidence: 99%

“…Some researchers combined CS with other machine learning methods, such as dictionary learning and random forest [18,20]. Furthermore, CS also has been used in the CPDP scenario [16,32]. However, how to set suitable cost values is still an unsolved problem for the cost-sensitive learning method.…”

Section: Related Workmentioning

confidence: 99%

Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance

2020

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Software defect prediction (SDP) is the technique used to predict the occurrences of defects in the early stages of software development process. Early prediction of defects will reduce the overall cost of software and also increase its reliability. Most of the defect prediction methods proposed in the literature suffer from the class imbalance problem. In this paper, a novel class imbalance reduction (CIR) algorithm is proposed to create a symmetry between the defect and non-defect records in the imbalance datasets by considering distribution properties of the datasets and is compared with SMOTE (synthetic minority oversampling technique), a built-in package of many machine learning tools that is considered a benchmark in handling class imbalance problems, and with K-Means SMOTE. We conducted the experiment on forty open source software defect datasets from PRedict or Models in Software Engineering (PROMISE) repository using eight different classifiers and evaluated with six performance measures. The results show that the proposed CIR method shows improved performance over SMOTE and K-Means SMOTE.

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“…Jing et al proposed unified metric representation (UMR) for the data of the source project and the target project, then they used canonical correlation analysis (CCA) to make the data distribution similar. Li et al proposed a new cost‐sensitive transfer kernel canonical correlation analysis method. This method can not only make the data distribution more similar but also utilize the different misclassification costs for defective and nondefective modules to alleviate the class imbalanced problem.…”

Section: Background and Related Workmentioning

confidence: 99%

Do different cross‐project defect prediction methods identify the same defective modules?

Chen

Qu³

et al. 2019

J Software Evolu Process

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Cross‐project defect prediction (CPDP) is needed when the target projects are new projects or the projects have less training data, since these projects do not have sufficient historical data to build high‐quality prediction models. The researchers have proposed many CPDP methods, and previous studies have conducted extensive comparisons on the performance of different CPDP methods. However, to the best of our knowledge, it remains unclear whether different CPDP methods can identify the same defective modules, and this issue has not been thoroughly explored. In this article, we select 12 state‐of‐the‐art CPDP methods, including eight supervised methods and four unsupervised methods. We first compare the performance of these methods in the same experiment settings on five widely used datasets (ie, NASA, SOFTLAB, PROMISE, AEEEM, and ReLink) and rank these methods via the Scott‐Knott test. Final results confirm the competitiveness of unsupervised methods. Then we perform diversity analysis on defective modules for these methods by using the McNemar test. Empirical results verify that different CPDP methods may lead to difference in the modules predicted as defective, especially when the comparison is performed between the supervised methods and unsupervised methods. Finally, we also find there exist a certain number of defective modules, which cannot be correctly identified by any of the CPDP methods or can be correctly identified by only one CPDP method. These findings can be utilized to design more effective methods to further improve the performance of CPDP.

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Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction

Cited by 99 publications

References 90 publications

Unsupervised Deep Domain Adaptation for Heterogeneous Defect Prediction

Unsupervised Deep Domain Adaptation for Heterogeneous Defect Prediction

Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance

Do different cross‐project defect prediction methods identify the same defective modules?

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