The DOI matches its Crossref record.
Dealing with noise in defect prediction
Abstract
Many software defect prediction models have been built using historical defect data obtained by mining software repositories (MSR). Recent studies have discovered that data so collected contain noises because current defect collection practices are based on optional bug fix keywords or bug report links in change logs. Automatically collected defect data based on the change logs could include noises.
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Cited by
An Empirical Comparison of Model Validation Techniques for Defect Prediction Models
Tantithamthavorn, C., McIntosh, S., Hassan, A. E., & Matsumoto, K. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2584050 · Cited by 574
Design Rule Spaces: A New Model for Representing and Analyzing Software Architecture
Cai, Y., Xiao, L., Kazman, R., Mo, R., & Feng, Q. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2797899 · Cited by 39
Metadata
- Authors
- Kim, S., Zhang, H., Wu, R., & Gong, L.
- Venue
- Proceedings of the 33rd International Conference on Software Engineering
- Publisher
- ACM
- Published
- 2011-05-21
- Pages
- 481-490
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2172232422
- Cited by
- 330
- References
- 0
- FWCI
- 11.06
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Dealing with noise in defect prediction. https://app.cochat.ai/papers/10.1145/1985793.1985859
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- Kim, S., Zhang, H., Wu, R., & Gong, L. (2011). Dealing with noise in defect prediction. Proceedings of the 33rd International Conference on Software Engineering. https://doi.org/10.1145/1985793.1985859
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
