Cross-Project Defect Prediction Using a Credibility Theory Based Naive Bayes Classifier

Poon, Wai Nam; Bennin, Kwabena Ebo; Huang, Jianglin; Phannachitta, Passakorn; Keung, Jacky

doi:10.1109/qrs.2017.53

Cited by 20 publications

(23 citation statements)

References 16 publications

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“…As discussed earlier, the CCCP model is built using the training data of one company and applied to the target data of another company (Zhang et al, 2017); however, Cross-company predictive models ex-perience great challenges, such as (i) to deal with the heterogeneity between the training and target data (Zhang, Audris, Ying, Foutse, & Ahmed, 2013), and (ii) dataset migration problem (i.e, moving data from one location to another usually required extra steps to transform according to the target source) between the cross-companies (Li, Huang, Wang, & Fang, 2017). Therefore, in such cases, the predictions mostly result in poor performance (Poon, Bennin, Huang, Phannachitta, & Keung, 2017). In order to address the aforementioned issues, the researchers have focused on the data transformation (DT) methods.…”

Section: Introductionmentioning

confidence: 99%

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Amin

Shah

Khattak

et al. 2019

International Journal of Information Management

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Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean).

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Section: Introductionmentioning

confidence: 99%

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Amin

Shah

Khattak

et al. 2019

International Journal of Information Management

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“…Ma et al proposed the transfer naive Bayes (TNB) method, which can assign weights for the instances in the source project. Poon et al proposed a credibility theory–based naive Bayes method to establish a novel reweighting mechanism between the source project and the target project. (d) Some researchers focus on feature mapping and selection .…”

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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“…Their results suggested that those instances, which had strong local knowledge, could be identified via nearest neighbors with the same class label. Poon et al [33] proposed a credibility theory based naive Bayes (CNB) classifier to establish a novel reweighting mechanism between the source projects and target projects, so that the source data could simultaneously adapt to the target data distribution and retain its own pattern. The experimental results demonstrate the significant improvement in terms of the performance metrics considered achieved by CNB over other CPDP approaches.…”

Section: Training Data Selection For Cpdpmentioning

confidence: 99%

An Improved Method for Cross-Project Defect Prediction by Simplifying Training Data

et al. 2018

Mathematical Problems in Engineering

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Cross-project defect prediction (CPDP) on projects with limited historical data has attracted much attention. To the best of our knowledge, however, the performance of existing approaches is usually poor, because of low quality cross-project training data. The objective of this study is to propose an improved method for CPDP by simplifying training data, labeled as TDSelector, which considers both the similarity and the number of defects that each training instance has (denoted by defects), and to demonstrate the effectiveness of the proposed method. Our work consists of three main steps. First, we constructed TDSelector in terms of a linear weighted function of instances’ similarity and defects. Second, the basic defect predictor used in our experiments was built by using the Logistic Regression classification algorithm. Third, we analyzed the impacts of different combinations of similarity and the normalization of defects on prediction performance and then compared with two existing methods. We evaluated our method on 14 projects collected from two public repositories. The results suggest that the proposed TDSelector method performs, on average, better than both baseline methods, and the AUC values are increased by up to 10.6% and 4.3%, respectively. That is, the inclusion of defects is indeed helpful to select high quality training instances for CPDP. On the other hand, the combination of Euclidean distance and linear normalization is the preferred way for TDSelector. An additional experiment also shows that selecting those instances with more bugs directly as training data can further improve the performance of the bug predictor trained by our method.

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Cross-Project Defect Prediction Using a Credibility Theory Based Naive Bayes Classifier

Cited by 20 publications

References 16 publications

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

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

An Improved Method for Cross-Project Defect Prediction by Simplifying Training Data

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