The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
An under‐sampled software defect prediction method based on hybrid multi‐objective cuckoo search
Abstract
Summary: Both the problem of class imbalance in datasets and parameter selection of Support Vector Machine (SVM) are crucial to predict software defects. However, there is no one working to solve these problems synchronously at present. To tackle this problem, a hybrid multi‐objective cuckoo search under‐sampled software defect prediction model based on SVM (HMOCS‐US‐SVM) is proposed to solve synchronously above two problems. Firstly, a hybrid multi‐objective cuckoo search with dynamical local search (HMOCS) is utilized to select synchronously the non‐defective sampling and optimize the parameters of SVM. Then, three under‐sampled methods for decision region range are proposed to select the non‐defective modules. In the simulation, the three indicators, including the false positive rate (pf), the probability of detection (pd), and G‐mean, are employed to measure the performance of the proposed algorithm. In addition, eight datasets from Promise database are selected to verify the proposed software defect predication model. Comparing with the result of eight prediction models, the proposed method comes into effect on solving software defect prediction problem.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
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 critique of software defect prediction models
Fenton, N. E., & Neil, M. (1999) · IEEE Transactions on Software Engineering · doi:10.1109/32.815326 · Cited by 1,040
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
Detection of Malicious Code Variants Based on Deep Learning
Cui, Z., Xue, F., Cai, X., Cao, Y., Wang, G.-G., & Chen, J. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2822680 · Cited by 597
Self-citationUsing Class Imbalance Learning for Software Defect Prediction
Wang, S., & Yao, X. (2013) · IEEE Transactions on Reliability · doi:10.1109/tr.2013.2259203 · Cited by 571
Boehm (1976) · IEEE Transactions on Computers · doi:10.1109/tc.1976.1674590 · Cited by 544
Firefly algorithm with neighborhood attraction
Wang, H., Wang, W., Zhou, X., Sun, H., Zhao, J., Yu, X., & Cui, Z. (2017) · Information Sciences · doi:10.1016/j.ins.2016.12.024 · Cited by 315
Self-citationA novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber–physical systems
Cui, Z., Sun, B., Wang, G., Xue, Y., & Chen, J. (2017) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2016.10.011 · Cited by 242
Self-citationA pigeon-inspired optimization algorithm for many-objective optimization problems
Cui, Z., Zhang, J., Wang, Y., Cao, Y., Cai, X., Zhang, W., & Chen, J. (2019) · Science China Information Sciences · doi:10.1007/s11432-018-9729-5 · Cited by 233
Self-citationCui, Z., Cao, Y., Cai, X., Cai, J., & Chen, J. (2019) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2017.12.014 · Cited by 211
Self-citationWang, G.-G., Cai, X., Cui, Z., Min, G., & Chen, J. (2017) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2017.2703784 · Cited by 194
Self-citationSample-based software defect prediction with active and semi-supervised learning
Li, M., Zhang, H., Wu, R., & Zhou, Z. (2011) · Automated Software Engineering · doi:10.1007/s10515-011-0092-1 · Cited by 188
Malicious code detection based on CNNs and multi-objective algorithm
Cui, Z., Du, L., Wang, P., Cai, X., & Zhang, W. (2019) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2019.03.010 · Cited by 182
Self-citationHybrid multi-objective cuckoo search with dynamical local search
Zhang, M., Wang, H., Cui, Z., & Chen, J. (2018) · Memetic Computing · doi:10.1007/s12293-017-0237-2 · Cited by 172
Self-citationMulti-objective Cross-Project Defect Prediction
Canfora, G., De Lucia, A., Di Penta, M., Oliveto, R., Panichella, A., & Panichella, S. (2013) · 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation · doi:10.1109/icst.2013.38 · Cited by 171
Siers, M. J., & Islam, M. Z. (2015) · Information Systems · doi:10.1016/j.is.2015.02.006 · Cited by 165
Attribute Selection and Imbalanced Data: Problems in Software Defect Prediction
Khoshgoftaar, T. M., Gao, K., & Seliya, N. (2010) · 2010 22nd IEEE International Conference on Tools with Artificial Intelligence · doi:10.1109/ictai.2010.27 · Cited by 164
Improved NSGA-III with selection-and-elimination operator
Cui, Z., Chang, Y., Zhang, J., Cai, X., & Zhang, W. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.05.011 · Cited by 160
Self-citationImproved bat algorithm with optimal forage strategy and random disturbance strategy
Cai, X., Gao, X. Z., & Xue, Y. (2016) · International Journal of Bio-Inspired Computation · doi:10.1504/ijbic.2016.078666 · Cited by 159
Self-citationApplying Novel Resampling Strategies To Software Defect Prediction
Pelayo, L., & Dick, S. (2007) · NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society · doi:10.1109/nafips.2007.383813 · Cited by 158
Two-Stage Cost-Sensitive Learning for Software Defect Prediction
Liu, M., Miao, L., & Zhang, D. (2014) · IEEE Transactions on Reliability · doi:10.1109/tr.2014.2316951 · Cited by 133
Tackling class overlap and imbalance problems in software defect prediction
Chen, L., Fang, B., Shang, Z., & Tang, Y. (2016) · Software Quality Journal · doi:10.1007/s11219-016-9342-6 · Cited by 127
Improving Software-Quality Predictions With Data Sampling and Boosting
Seiffert, C., Khoshgoftaar, T. M., & Hulse, J. V. (2009) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2009.2027131 · Cited by 113
Bat algorithm with principal component analysis
Cui, Z., Li, F., & Zhang, W. (2018) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-018-0888-4 · Cited by 90
Self-citationEvolutionary Sampling and Software Quality Modeling of High-Assurance Systems
Drown, D. J., Khoshgoftaar, T. M., & Seliya, N. (2009) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2009.2020804 · Cited by 72
Ensemble Methods for Class Imbalance Learning
Liu, X., & Zhou, Z. (2013) · Wiley eBooks · doi:10.1002/9781118646106.ch4 · Cited by 69
Tuning magnetism by strain and external electric field in zigzag Janus MoSSe nanoribbons
Wang, M., Pang, Y., Liu, D. Y., Zheng, S., & Song, Q. (2018) · Computational Materials Science · doi:10.1016/j.commatsci.2018.01.044 · Cited by 42
Research of Software Defect Prediction Model Based on ACO-SVM
Jiang, H., Zong, M., & Xiang-Ying, L. I. U. (2011) · Chinese Journal of Computers · doi:10.3724/sp.j.1016.2011.01148 · Cited by 19
Dong, H., & Jian, G. (2015) · Cybernetics and Information Technologies · doi:10.1515/cait-2015-0047 · Cited by 14
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Self-citation
Metadata
- Authors
- Cai, X., Niu, Y., Geng, S., Zhang, J., Cui, Z., Li, J., & Chen, J.
- Venue
- Concurrency and Computation: Practice and Experience
- Publisher
- Wiley
- Published
- 2019-10-18
- Volume
- 32
- Issue
- 5
- ISSN
- 1532-0626 (ISSN-L)
- 1532-0634
- OpenAlex
- W2981073616
- Cited by
- 267
- References
- 31
- FWCI
- 59.36
- Type
- journal article
- Language
- en
- Open access
- closed
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