The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Transfer defect learning
Abstract
Many software defect prediction approaches have been proposed and most are effective in within-project prediction settings. However, for new projects or projects with limited training data, it is desirable to learn a prediction model by using sufficient training data from existing source projects and then apply the model to some target projects (cross-project defect prediction). Unfortunately, the performance of cross-project defect prediction is generally poor, largely because of feature distribution differences between the source and target projects. In this paper, we apply a state-of-the-art transfer learning approach, TCA, to make feature distributions in source and target projects similar. In addition, we propose a novel transfer defect learning approach, TCA+, by extending TCA. Our experimental results for eight open-source projects show that TCA+ significantly improves cross-project prediction performance.
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Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Self-citationIndividual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
Analysis of Representations for Domain Adaptation
Ben-David, S., Blitzer, J., Crammer, K., & Pereira, F. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0022 · Cited by 2,019
Cited by
Software Defect Prediction via Convolutional Neural Network
Li, J., He, P., Zhu, J., & Lyu, M. R. (2017) · 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs.2017.42 · Cited by 462
An empirical study on software defect prediction with a simplified metric set
He, P., Li, B., Liu, X., Chen, J., & Ma, Y. (2015) · Information and Software Technology · doi:10.1016/j.infsof.2014.11.006 · Cited by 309
HYDRA: Massively Compositional Model for Cross-Project Defect Prediction
Xia, X., Lo, D., Pan, S. J., Nagappan, N., & Wang, X. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2543218 · Cited by 293
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Metadata
- Authors
- Nam, J., Pan, S. J., & Kim, S.
- Venue
- 2013 35th International Conference on Software Engineering (ICSE)
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 382-391
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W4238859253
- Cited by
- 449
- References
- 3
- FWCI
- 15.60
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Nam, J., Pan, S. J., & Kim, S. (2013). Transfer defect learning. 2013 35th International Conference on Software Engineering (ICSE). https://doi.org/10.1109/icse.2013.6606584
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