scholarly journals An Empirical Study of Classifier Combination for Cross-Project Defect Prediction

2015 ◽  
Author(s):  
Yun Zhang ◽  
David Lo ◽  
Xin Xia ◽  
Jianling Sun
Keyword(s):  
Empirical Study ◽  
Defect Prediction ◽  
Classifier Combination ◽  
Cross Project

scholarly journals An Empirical Study on Software Defect Prediction Using CodeBERT Model

2021 ◽  
Vol 11 (11) ◽  
pp. 4793
Author(s):  
Cong Pan ◽  
Minyan Lu ◽  
Biao Xu
Keyword(s):  
Deep Learning ◽  
Software Engineering ◽  
Empirical Study ◽  
Empirical Studies ◽  
Language Model ◽  
Prediction Performance ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Cross Project

Deep learning-based software defect prediction has been popular these days. Recently, the publishing of the CodeBERT model has made it possible to perform many software engineering tasks. We propose various CodeBERT models targeting software defect prediction, including CodeBERT-NT, CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT. We perform empirical studies using such models in cross-version and cross-project software defect prediction to investigate if using a neural language model like CodeBERT could improve prediction performance. We also investigate the effects of different prediction patterns in software defect prediction using CodeBERT models. The empirical results are further discussed.

scholarly journals Too trivial to test? An inverse view on defect prediction to identify methods with low fault risk

2019 ◽  
Vol 5 ◽  
pp. e187 ◽  
Author(s):  
Rainer Niedermayr ◽  
Tobias Röhm ◽  
Stefan Wagner
Keyword(s):  
Empirical Study ◽  
Association Rule ◽  
Association Rule Mining ◽  
Defect Prediction ◽  
Efficient Allocation ◽  
Rule Mining ◽  
Scarce Resources ◽  
Development Teams ◽  
Code Metrics ◽  
Cross Project

BackgroundTest resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to identify fault-prone code regions. However, defect prediction tends to low precision in cross-project prediction scenarios.AimsWe take an inverse view on defect prediction and aim to identify methods that can be deferred when testing because they contain hardly any faults due to their code being “trivial”. We expect that characteristics of such methods might be project-independent, so that our approach could improve cross-project predictions.MethodWe compute code metrics and apply association rule mining to create rules for identifying methods with low fault risk (LFR). We conduct an empirical study to assess our approach with six Java open-source projects containing precise fault data at the method level.ResultsOur results show that inverse defect prediction can identify approx. 32–44% of the methods of a project to have a LFR; on average, they are about six times less likely to contain a fault than other methods. In cross-project predictions with larger, more diversified training sets, identified methods are even 11 times less likely to contain a fault.ConclusionsInverse defect prediction supports the efficient allocation of test resources by identifying methods that can be treated with less priority in testing activities and is well applicable in cross-project prediction scenarios.

scholarly journals Combined classifier for cross-project defect prediction: an extended empirical study

2018 ◽  
Vol 12 (2) ◽  
pp. 280-296 ◽  
Author(s):  
Yun Zhang ◽  
David Lo ◽  
Xin Xia ◽  
Jianling Sun
Keyword(s):  
Empirical Study ◽  
Defect Prediction ◽  
Combined Classifier ◽  
Cross Project

scholarly journals Too trivial to test? An inverse view on defect prediction to identify methods with low fault risk

2018 ◽  
Author(s):  
Rainer Niedermayr ◽  
Tobias Röhm ◽  
Stefan Wagner
Keyword(s):  
Empirical Study ◽  
Association Rule ◽  
Association Rule Mining ◽  
Defect Prediction ◽  
Efficient Allocation ◽  
Rule Mining ◽  
Scarce Resources ◽  
Development Teams ◽  
Code Metrics ◽  
Cross Project

Background. Test resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to identify fault-prone code regions. However, defect prediction tends to low precision in cross-project prediction scenarios. Aims. We take an inverse view on defect prediction and aim to identify methods that can be deferred when testing because they contain hardly any faults due to their code being "trivial". We expect that characteristics of such methods might be project-independent, so that our approach could improve cross-project predictions. Method. We compute code metrics and apply association rule mining to create rules for identifying methods with low fault risk. We conduct an empirical study to assess our approach with six Java open-source projects containing precise fault data at the method level. Results. Our results show that inverse defect prediction can identify approx. 32-44% of the methods of a project to have a low fault risk; on average, they are about six times less likely to contain a fault than other methods. In cross-project predictions with larger, more diversified training sets, identified methods are even eleven times less likely to contain a fault. Conclusions. Inverse defect prediction supports the efficient allocation of test resources by identifying methods that can be treated with less priority in testing activities and is well applicable in cross-project prediction scenarios.

scholarly journals An Empirical Study on the Effectiveness of Feature Selection for Cross-Project Defect Prediction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 35710-35718 ◽  
Author(s):  
Qiao Yu ◽  
Junyan Qian ◽  
Shujuan Jiang ◽  
Zhenhua Wu ◽  
Gongjie Zhang
Keyword(s):  
Feature Selection ◽  
Empirical Study ◽  
Defect Prediction ◽  
Selection For ◽  
Cross Project
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