Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning

Jing, Xiao‐Yuan; Wu, Fei; Dong, Xiwei; Qi, Fumin; Xu, Bin

doi:10.1145/2786805.2786813

Cited by 163 publications

(165 citation statements)

References 41 publications

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“…Crosscompany defect prediction (CCDP) is a practical approach to solve the problem. It trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company [22].…”

Section: Doi Reference Number: 1018293/seke2017-043mentioning

confidence: 99%

“…The second mainstream way is to design effective defect predictor based on transfer learning techniques (e.g., [22,27,28]). For instance, Ma et al [27] proposed a novel algorithm called Transfer Naive Bayes (TNB) to transfer cross-company data information into the weights of the training data and then build the predictor based on re-weighted CC data.…”

Section: B Cross-company Defect Predictionmentioning

confidence: 99%

“…The heterogeneous CCDP (HCCDP) task is that the source and target company data is heterogeneous. Jing et al [22] provided an effective solution for HCCDP. They proposed a unified metric representation (UMR) for the data of source and target companies and introduced canonical correlation analysis (CCA), an effective transfer learning method, into CCDP to make the data distributions of source and target companies similar.…”

Section: B Cross-company Defect Predictionmentioning

confidence: 99%

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A Data Filtering Method Based on Agglomerative Clustering

Yu¹,

Peipei²,

Zhang³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, larger irrelevant crosscompany (CC) data usually makes it difficult to build a crosscompany defect prediction model with high performance. To address such issues, this paper proposes a data filtering method based on Agglomerative Clustering (DFAC) for cross-company defect prediction. First, DFAC combines within-company instances and cross-company instances and uses Agglomerative clustering algorithms to group these instances. Second, DFAC selects sub-clusters which consist at least one WC instance, and collects the CC instances in the selected sub-clusters into a new CC data. Compared with existing data filter methods, the experimental results on 15 public PROMISE datasets show that DFAC increases PD value, reduces PF value and achieves higher G-measure and AUC values.

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Section: Doi Reference Number: 1018293/seke2017-043mentioning

confidence: 99%

Section: B Cross-company Defect Predictionmentioning

confidence: 99%

Section: B Cross-company Defect Predictionmentioning

confidence: 99%

See 1 more Smart Citation

A Data Filtering Method Based on Agglomerative Clustering

Yu¹,

Peipei²,

Zhang³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Is the Number of Faults Helpful for Cross-Company Defect Prediction?

Jing¹,

Zhang²,

Jin³

2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-In the field of Cross-company Defect Prediction (CCDP), how to deal with the data to make it more accurately predict the cross-company software defects is the focus problem we need to consider. Now a mainstream idea is to determine the weight of the training data based on the similarity between the training data and the test set, and then build the model on the basis of these weighted data. However, sometimes, when we deal with some problems with imbalance class, directly using the weight calculated above may lead to errors. Because a large number of non-defective instance's weight accumulation will lead to the defective instance's weight has a little impact on the final result. This is why we need to consider the addition of the number of defects. Considering the number of defects will effectively eliminate the impact of a large number of non-defective instance's weight accumulation. Therefore, we propose a Transfer-learning Naï ve Bayes model considering the number of defective information(NTNB). The method consists of two major stages: weight the data and build the prediction model. In the stage of weighting the data, we not only consider the similarity between the data but also consider the number of defects to get the final weights for data. And we conducted a set of comparative experiments on six open crosscompany datasets. The results show that considering the number of defects information can effectively avoid some defective instance is misjudged as non-defective instance, and improve the accuracy of prediction in some unbalanced problems.

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“…Crosscompany defect prediction (CCDP) is a practical approach to solve the problem. 1 It trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company [7].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Source TrAdaBoost Approach for Cross-Company Defect Prediction

Liu

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, larger irrelevant crosscompany (CC) data usually makes it difficult to build a prediction model with high performance. On the other hand, brute force leveraging of CC data poorly related to withincompany (WC) data may decrease the prediction model performance. To address such issues, this paper introduces Multi-Source TrAdaBoost algorithm, an effective transfer learning approach to perform CCDP. The core idea of our approach is that: 1) employ limited amount of labeled WC data to weaken the impact of irrelevant CC data; 2) import knowledge not from one but from multiple sources to avoid negative transfer. The experimental results indicate that: 1) our proposed approach achieves the best overall performance among all tested CCDP approaches; 2) only 10% labeled WC data is enough to achieve good performance of CCDP by using our proposed approach.

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Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning

Cited by 163 publications

References 41 publications

A Data Filtering Method Based on Agglomerative Clustering

A Data Filtering Method Based on Agglomerative Clustering

Is the Number of Faults Helpful for Cross-Company Defect Prediction?

A Multi-Source TrAdaBoost Approach for Cross-Company Defect Prediction

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