The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
An extensive comparison of bug prediction approaches
Abstract
Reliably predicting software defects is one of software engineering's holy grails. Researchers have devised and implemented a plethora of bug prediction approaches varying in terms of accuracy, complexity and the input data they require. However, the absence of an established benchmark makes it hard, if not impossible, to compare approaches. We present a benchmark for defect prediction, in the form of a publicly available data set consisting of several software systems, and provide an extensive comparison of the explanative and predictive power of well-known bug prediction approaches, together with novel approaches we devised. Based on the results, we discuss the performance and stability of the approaches with respect to our benchmark and deduce a number of insights on bug prediction models.
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Citations
Cites
Use of relative code churn measures to predict system defect density
Nagappan, N., & Ball, T. (2005) · Proceedings of the 27th international conference on Software engineering - ICSE '05 · doi:10.1145/1062455.1062514 · Cited by 775
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A Systematic Literature Review on Fault Prediction Performance in Software Engineering
Hall, T., Beecham, S., Bowes, D., Gray, D., & Counsell, S. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2011.103 · Cited by 1,154
A systematic review of machine learning techniques for software fault prediction
Malhotra, R. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2014.11.023 · Cited by 620
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
An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models
Jiarpakdee, J., Tantithamthavorn, C. K., Dam, H. K., & Grundy, J. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.2982385 · Cited by 148
Predicting Defective Lines Using a Model-Agnostic Technique
Wattanakriengkrai, S., Thongtanunam, P., Tantithamthavorn, C., Hata, H., & Matsumoto, K. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3023177 · Cited by 137
Predicting Design Impactful Changes in Modern Code Review: A Large-Scale Empirical Study
Uchoa, A., Barbosa, C., Coutinho, D., Oizumi, W., Assuncao, W. K. G., Vergilio, S. R., Pereira, J. A., Oliveira, A., & Garcia, A. (2021) · 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr52588.2021.00059 · Cited by 42
Pitfalls and guidelines for using time-based Git data
Flint, S. W., Chauhan, J., & Dyer, R. (2022) · Empirical Software Engineering · doi:10.1007/s10664-022-10200-y · Cited by 9
Entropy Guided Spectrum Based Bug Localization Using Statistical Language Model
Chakraborty, S., Li, Y., Irvine, M., Saha, R., & Ray, B. (2018) · arXiv · doi:10.48550/arxiv.1802.06947 · Cited by 7
“This AUCEC metric is a non-parametric metric similar to the ROC curve and does not depend on bug distribution [45], thus becomes standard in bug-localization literature [16].”
Method
Metadata
- Authors
- D'Ambros, M., Lanza, M., & Robbes, R.
- Venue
- 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)
- Publisher
- IEEE
- Published
- 2010-05
- Pages
- 31-41
- OpenAlex
- W2163732854
- Cited by
- 637
- References
- 1
- FWCI
- 15.74
- Type
- conference paper
- Language
- en
- Open access
- closed
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