The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Robust Prediction of Fault-Proneness by Random Forests
Abstract
Accurate prediction of fault prone modules (a module is equivalent to a C function or a C+ + method) in software development process enables effective detection and identification of defects. Such prediction models are especially beneficial for large-scale systems, where verification experts need to focus their attention and resources to problem areas in the system under development. This paper presents a novel methodology for predicting fault prone modules, based on random forests. Random forests are an extension of decision tree learning. Instead of generating one decision tree, this methodology generates hundreds or even thousands of trees using subsets of the training data. Classification decision is obtained by voting. We applied random forests in five case studies based on NASA data sets. The prediction accuracy of the proposed methodology is generally higher than that achieved by logistic regression, discriminant analysis and the algorithms in two machine learning software packages, WEKA [I. H. Witten et al. (1999)] and See5. The difference in the performance of the proposed methodology over other methods is statistically significant. Further, the classification accuracy of random forests is more significant over other methods in larger data sets.
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Cites
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A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
Witten, I. H. (2008) · Cited by 9,143
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A validation of object-oriented design metrics as quality indicators
Basili, V. R., Briand, L. C., & Melo, W. L. (1996) · IEEE Transactions on Software Engineering · doi:10.1109/32.544352 · Cited by 1,726
Weka: Practical machine learning tools and techniques with Java implementations
Witten, I. H., Frank, E., Trigg, L. E., Hall, M. A., Holmes, G., & Cunningham, S. J. (1999) · Research Commons (University of Waikato) · Cited by 629
The detection of fault-prone programs
Munson, J. C., & Khoshgoftaar, T. M. (1992) · IEEE Transactions on Software Engineering · doi:10.1109/32.135775 · Cited by 442
Design complexity measurement and testing
McCabe, T. J., & Butler, C. W. (1989) · Communications of the ACM · doi:10.1145/76380.76382 · Cited by 364
Methodology for validating software metrics
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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
Predicting the location and number of faults in large software systems
Ostrand, T. J., Weyuker, E. J., & Bell, R. M. (2005) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2005.49 · Cited by 720
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
Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection
Zhang, J., & Zulkernine, M. (2006) · 2006 IEEE International Conference on Communications · doi:10.1109/icc.2006.255127 · Cited by 256
Metadata
- Authors
- Guo, L., Ma, Y., Cukic, B., & Singh, H.
- Venue
- 15th International Symposium on Software Reliability Engineering
- Publisher
- IEEE
- Published
- 2005-04-06
- Pages
- 417-428
- ISSN
- 1071-9458 (ISSN-L)
- OpenAlex
- W1554055400
- Cited by
- 271
- References
- 18
- FWCI
- 10.17
- Type
- conference paper
- Language
- en
- Open access
- closed
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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.
