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
Cited by
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
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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- D'Ambros, M., Lanza, M., & Robbes, R. (2010). An extensive comparison of bug prediction approaches. 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010). https://doi.org/10.1109/msr.2010.5463279
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