Heterogeneous Defect Prediction

Nam, Jaechang; Wei, Fu; Kim, Sunghun; Menzies, Tim; Tan, Lin

doi:10.1109/tse.2017.2720603

Cited by 268 publications

(239 citation statements)

References 71 publications

(138 reference statements)

Supporting

Mentioning

231

Contrasting

Unclassified

Order By: Relevance

“…When obtaining the transform matrix P , we can obtain the projected source data

{\overset{X}{true}}_{s} = P^{T} X_{s}

. On the basis of the mixed project data

{\overset{X}{true}}_{s}

and

{\overset{X}{true}}_{l}

, we can build the prediction model with LR classifier, which has been used in the fault prediction studies . The LR is implemented in LIBLINEAR (an award‐winning library for large linear classification) toolbox.…”

Section: Approachmentioning

confidence: 99%

“…However, existing CPFP methods require that the instances of source and target projects have the common metrics (features), ie, the metric sets should be identical between projects. In practice, there are very few common metrics between source and target projects for many cases, and finding other projects with multiple common metrics can be challenging . Table shows the number of metrics in defect data from 5 groups, including NASA , SOFTLAB , ReLink , AEEEM , and PROMISE .…”

Section: Introductionmentioning

confidence: 99%

“…Recently, heterogeneous fault prediction (HFP) models are presented to predict faults across projects with heterogeneous metric sets, ie, source and target projects have different metric sets. For example, Jing et al presented a transfer CCA (canonical correlation analysis)+ method by using unified metric representation (UMR) and CCA‐based transfer learning technique for HFP.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Jing et al presented a transfer CCA (canonical correlation analysis)+ method by using unified metric representation (UMR) and CCA‐based transfer learning technique for HFP. Nam and Kim used metric selection and matching techniques to predict faults across projects with heterogeneous metric sets.…”

Section: Introductionmentioning

confidence: 99%

“…Although existing HFP methods have achieved promising results, the following issues still have not been well studied. (1)Mixed project data problem : In the early phases of software testing, projects may have only a small amount of historical defect data (training target data).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Heterogeneous fault prediction with cost‐sensitive domain adaptation

Jing

Zhu

2018

Software Testing Verif & Rel

View full text Add to dashboard Cite

Summary In the early phases of software testing, projects may have only limited historical defect data. Learning prediction model with such insufficient training data will limit the efficacy of learned predictor. In practice, there are usually many publicly available fault prediction datasets. Recently, heterogeneous fault prediction (HFP) has been proposed. However, existing HFP models do not investigate how to use mixed project data to predict target. Furthermore, defect data are often imbalanced. The imbalanced data distribution of source usually leads to serious misclassification of fault‐prone instances, which will degrade the predictor's performance. Existing HFP methods do not consider the class imbalance problem in the training stages. In this paper, we propose a novel Cost‐sensitive Label and Structure‐consistent Unilateral Projection (CLSUP) approach for HFP. CLSUP can not only make better use of the within‐project and cross‐project data but also alleviate the class imbalance problem by setting different misclassification costs for fault‐prone and non–fault‐prone instances. Extensive experiments on 30 projects demonstrate the effectiveness of CLSUP.

show abstract

“…When obtaining the transform matrix P , we can obtain the projected source data

{\overset{X}{true}}_{s} = P^{T} X_{s}

. On the basis of the mixed project data

{\overset{X}{true}}_{s}

and

{\overset{X}{true}}_{l}

Section: Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Heterogeneous fault prediction with cost‐sensitive domain adaptation

Jing

Zhu

2018

Software Testing Verif & Rel

View full text Add to dashboard Cite

show abstract

Multitask defect prediction

Chen

Xia

et al. 2019

J Software Evolu Process

View full text Add to dashboard Cite

Within‐project defect prediction assumes that we have sufficient labeled data from the same project, while cross‐project defect prediction assumes that we have plenty of labeled data from source projects. However, in practice, we might only have limited labeled data from both the source and target projects in some scenarios. In this paper, we want to apply multitask learning to investigate such a new scenario. To our best knowledge, this problem (ie, both the source project and the target project have limited labeled data) has not been thoroughly investigated, and we are the first to propose a novel multitask defect prediction approach mask. mask consists of a differential evolution optimization phase and a multitask learning phase. The former phase aims to find optimal weights for shared and nonshared information in related projects (ie, the target project and its related source projects), while the latter phase builds prediction models for each project simultaneously. To verify the effectiveness of mask, we perform experimental studies on 18 real‐world software projects and compare our approach with four state‐of‐the‐art baseline approaches: single‐task learning (STL), simple combined learning (SCL), Peters filter, and Burak filter. Experimental results show that mask can achieve F1 of 0.397 and AUC of 0.608 on average with a few labeled data (ie, 10% of data). Across the 18 projects, mask can outperform baseline methods significantly in terms of F1 and AUC. Therefore, by utilizing the relatedness among multiple projects, mask can perform significantly better than the state‐of‐the‐art methods. The results confirm that mask is promising for software defect prediction when the source and target projects both have limited training data.

show abstract

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

Chen

Qu³

et al. 2019

J Software Evolu Process

View full text Add to dashboard Cite

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.

show abstract

Heterogeneous Defect Prediction

Cited by 268 publications

References 71 publications

Heterogeneous fault prediction with cost‐sensitive domain adaptation

Heterogeneous fault prediction with cost‐sensitive domain adaptation

Multitask defect prediction

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

Contact Info

Product

Resources

About