Genetic Algorithm-based Transfer Learning for Cross-Company Software Defect Prediction

Ren, Shengbing; Zhang, Zhen; Liu, Yuan; Xie, Ruliang

doi:10.14257/ijhit.2017.10.3.05

Cited by 5 publications

(7 citation statements)

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“…Many researchers have stated in their papers that identifying defects in software as early as possible has great economic value [12]. Defect prediction is to establish a prediction model based on a software defect metric element in the early stage of defect detection.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the difficulty of collecting the historical data, some new projects do not have historical data. Many researchers focus on cross-project defect prediction [9,12,13], and a series of classical CPDP methods are proposed. The key factor of CPDP is how to obtain the same distribution of data as the target set by transferring learning in the CPDP method.…”

Section: Related Workmentioning

confidence: 99%

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Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction

et al. 2019

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Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach. However, traditional defect prediction methods use feature attributes to represent samples, which cannot avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space (DM-CPDP). This method not only retains the original information, but also obtains the relationship with other objects. So it can enhances the discriminant ability of the sample attributes to the class label. This method firstly uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to calculate the sample dissimilarities between the prototype set and the source domain or the target set to form the dissimilarity space. In this space, the training set is obtained with the earth mover’s distance (EMD) method. For the unlabeled samples converted from the target set, the k-Nearest Neighbor (KNN) algorithm is used to label those samples. Finally, the model is learned from training data based on TrAdaBoost method and used to predict new potential defects. The experimental results show that this approach has better performance than other traditional CPDP methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many researchers have stated in their papers that identifying defects in software as early as possible has great economic value [12]. Defect prediction is to establish a prediction model based on software defect metric element in the early stage of defect detection.…”

Section: Related Workmentioning

confidence: 99%

“…The kernel tools can represent a certain relationship between two objects. When relaxing the requirement for Mercer kernels, there are more powerful dissimilarity measures can be defined in the domain [12]. According to the analysis of arc-cosine kernel, and with the goal , and shown as:…”

Section: Prototype Selectionmentioning

confidence: 99%

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Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction

Ren¹,

Zhang²,

Munir³

et al. 2018

Preprint

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Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach.  However, traditional defect prediction methods using feature attributes to represent samples, which can not avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space ( DM-CPDP). This method first uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to form the dissimilarity space, and in this space the training set is obtained with the earth mover’s distance (EMD) method. For the unlabeled samples converted from the target set, the KNN algorithm is used to label those samples. Finally, we use TrAdaBoost method to establish the prediction model.  The experimental results show that our approach has better performance than other traditional CPDP methods.

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