A multi-objective effort-aware defect prediction approach based on NSGA-II
Tài liệu tham khảo
Yu, 2022, Predicting the precise number of software defects: Are we there yet?, Inf. Softw. Technol., 146, 10.1016/j.infsof.2022.106847
Yu, 2018, Cross-company defect prediction via semi-supervised clustering-based data filtering and MSTrA-based transfer learning, Soft Comput., 22, 3461, 10.1007/s00500-018-3093-1
Zhou, 2022, Software defect prediction with semantic and structural information of codes based on Graph Neural Networks, Inf. Softw. Technol., 152, 10.1016/j.infsof.2022.107057
Chen, 2022, Aligned metric representation based balanced multiset ensemble learning for heterogeneous defect prediction, Inf. Softw. Technol., 147, 10.1016/j.infsof.2022.106892
Sun, 2021, CFPS: Collaborative filtering based source projects selection for cross-project defect prediction, Appl. Soft Comput., 99, 10.1016/j.asoc.2020.106940
Zhao, 2022, ST-TLF: Cross-version defect prediction framework based transfer learning, Inf. Softw. Technol., 149, 10.1016/j.infsof.2022.106939
Yu, 2017, The Bayesian Network based program dependence graph and its application to fault localization, J. Syst. Softw., 134, 44, 10.1016/j.jss.2017.08.025
Yu, 2016, Bayesian network based program dependence graph for fault localization, 181
Zhang, 2023, Influential global and local contexts guided trace representation for fault localization, ACM Trans. Softw. Eng. Methodol., 32, 78:1, 10.1145/3576043
Bai, 2022, A three-stage transfer learning framework for multi-source cross-project software defect prediction, Inf. Softw. Technol., 150, 10.1016/j.infsof.2022.106985
Gao, 2022, Dealing with imbalanced data for interpretable defect prediction, Inf. Softw. Technol., 151, 10.1016/j.infsof.2022.107016
Stradowski, 2023, Industrial applications of software defect prediction using machine learning: A business-driven systematic literature review, Inf. Softw. Technol., 159, 10.1016/j.infsof.2023.107192
Sun, 2020, Collaborative filtering based recommendation of sampling methods for software defect prediction, Appl. Soft Comput., 90, 10.1016/j.asoc.2020.106163
Kamei, 2010, Revisiting common bug prediction findings using effort-aware models, 1
Li, 2023, Revisiting ‘revisiting supervised methods for effort-aware cross-project defect prediction’, IET Softw., 17, 472, 10.1049/sfw2.12133
Mende, 2010, Effort-aware defect prediction models, 107
Menzies, 2010, Defect prediction from static code features: current results, limitations, new approaches, Autom. Softw. Eng., 17, 375, 10.1007/s10515-010-0069-5
Huang, 2019, Revisiting supervised and unsupervised models for effort-aware just-in-time defect prediction, Empir. Softw. Eng., 24, 2823, 10.1007/s10664-018-9661-2
Ni, 2022, Revisiting supervised and unsupervised methods for effort-aware cross-project defect prediction, IEEE Trans. Softw. Eng., 48, 786, 10.1109/TSE.2020.3001739
Ni, 2022, Just-in-time defect prediction on JavaScript projects: A replication study, ACM Trans. Softw. Eng. Methodol., 31, 1, 10.1145/3508479
Li, 2020, Effort-aware semi-supervised just-in-time defect prediction, Inf. Softw. Technol., 126, 10.1016/j.infsof.2020.106364
Li, 2023, The impact of feature selection techniques on effort-aware defect prediction: An empirical study, IET Softw., 17, 168, 10.1049/sfw2.12099
Kamei, 2012, A large-scale empirical study of just-in-time quality assurance, IEEE Trans. Softw. Eng., 39, 757, 10.1109/TSE.2012.70
Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 182, 10.1109/4235.996017
Yang, 2014, Are slice-based cohesion metrics actually useful in effort-aware post-release fault-proneness prediction? An empirical study, IEEE Trans. Softw. Eng., 41, 331, 10.1109/TSE.2014.2370048
Menzies, 2012
Bennin, 2022, An empirical study on the effectiveness of data resampling approaches for cross-project software defect prediction, IET Softw., 16, 185, 10.1049/sfw2.12052
Feng, 2022, The impact of the distance metric and measure on SMOTE-based techniques in software defect prediction, Inf. Softw. Technol., 142, 10.1016/j.infsof.2021.106742
Pandey, 2021, An empirical study toward dealing with noise and class imbalance issues in software defect prediction, Soft Comput., 25, 13465, 10.1007/s00500-021-06096-3
Yu, 2019, Improving ranking-oriented defect prediction using a cost-sensitive ranking SVM, IEEE Trans. Reliab., 69, 139, 10.1109/TR.2019.2931559
Yu, 2017, Learning from imbalanced data for predicting the number of software defects, 78
Zheng, 2022, Interpretability application of the Just-in-Time software defect prediction model, J. Syst. Softw., 188, 10.1016/j.jss.2022.111245
Tantithamthavorn, 2020, The impact of class rebalancing techniques on the performance and interpretation of defect prediction models, IEEE Trans. Softw. Eng., 46, 1200, 10.1109/TSE.2018.2876537
Bowes, 2018, Software defect prediction: do different classifiers find the same defects?, Softw. Qual. J., 26, 525, 10.1007/s11219-016-9353-3
Ghotra, 2015, Revisiting the impact of classification techniques on the performance of defect prediction models, 789
Tantithamthavorn, 2018, The impact of automated parameter optimization on defect prediction models, IEEE Trans. Softw. Eng., 45, 683, 10.1109/TSE.2018.2794977
Kochhar, 2016, Practitioners’ expectations on automated fault localization, 165
Parnin, 2011, Are automated debugging techniques actually helping programmers?, 199
Chen, 2018, MULTI: Multi-objective effort-aware just-in-time software defect prediction, Inf. Softw. Technol., 93, 1, 10.1016/j.infsof.2017.08.004
Huang, 2017, Supervised vs unsupervised models: A holistic look at effort-aware just-in-time defect prediction, 159
Awla, 2023, A comparative evaluation of Bayesian networks structure learning using falcon optimization algorithm, Int. J. Interact. Multimedia Artif. Intell., 8, 81
Ding, 2022, Improved GWO algorithm for UAV path planning on crop pest monitoring, Int. J. Interact. Multimed. Artif. Intell., 7, 30
Chen, 2020, Supervised deep hashing with a joint deep network, Pattern Recognit., 105, 10.1016/j.patcog.2020.107368
Chen, 2020, Deep cross-modal image–voice retrieval in remote sensing, IEEE Trans. Geosci. Remote Sens., 58, 7049, 10.1109/TGRS.2020.2979273
Chen, 2022, Deep quadruple-based hashing for remote sensing image-sound retrieval, IEEE Trans. Geosci. Remote Sens., 60, 1, 10.1109/TGRS.2022.3231215
He, 2021, Characterizing research leadership on geographically weighted collaboration network, Scientometrics, 126, 4005, 10.1007/s11192-021-03943-w
He, 2022, Proximity-aware research leadership recommendation in research collaboration via deep neural networks, J. Assoc. Inf. Sci. Technol., 73, 70, 10.1002/asi.24546
Chen, 2021, Improving Ponzi scheme contract detection using multi-channel TextCNN and transformer, Sensors, 21, 6417, 10.3390/s21196417
Li, 2023, On the relative value of imbalanced learning for code smell detection, Softw. - Pract. Exp., 53, 1902, 10.1002/spe.3235
Ma, 2022, CASMS: Combining clustering with attention semantic model for identifying security bug reports, Inf. Softw. Technol., 147, 10.1016/j.infsof.2022.106906
Yang, 2023, On the significance of category prediction for code-comment synchronization, ACM Trans. Softw. Eng. Methodol., 32, 1, 10.1145/3534117
Yu, 2019, An empirical study of learning to rank techniques for effort-aware defect prediction, 298
Zhao, 2022, A compositional model for effort-aware Just-In-Time defect prediction on android apps, IET Softw., 16, 259, 10.1049/sfw2.12040
Yang, 2021, DEJIT: a differential evolution algorithm for effort-aware just-in-time software defect prediction, Int. J. Softw. Eng. Knowl. Eng., 31, 289, 10.1142/S0218194021500108
Yu, 2023, Finding the best learning to rank algorithms for effort-aware defect prediction, Inf. Softw. Technol., 157, 10.1016/j.infsof.2023.107165
Le, 2015, Beyond support and confidence: Exploring interestingness measures for rule-based specification mining, 331
Rahman, 2012, Clones: What is that smell?, Empir. Softw. Eng., 17, 503, 10.1007/s10664-011-9195-3
Tong, 2022, SHSE: A subspace hybrid sampling ensemble method for software defect number prediction, Inf. Softw. Technol., 142, 10.1016/j.infsof.2021.106747
Yan, 2017, File-level defect prediction: Unsupervised vs. supervised models, 344
Wilcoxon, 1992, Individual comparisons by ranking methods, 196
Ferreira, 2006, On the benjamini–hochberg method, Ann. Statist., 34, 1827, 10.1214/009053606000000425
Kampenes, 2007, A systematic review of effect size in software engineering experiments, Inf. Softw. Technol., 49, 1073, 10.1016/j.infsof.2007.02.015
Balogun, 2020, Impact of feature selection methods on the predictive performance of software defect prediction models: an extensive empirical study, Symmetry, 12, 1147, 10.3390/sym12071147
Ghotra, 2017, A large-scale study of the impact of feature selection techniques on defect classification models, 146
Ni, 2019, An empirical study on pareto based multi-objective feature selection for software defect prediction, J. Syst. Softw., 152, 215, 10.1016/j.jss.2019.03.012
Thirumoorthy, 2022, A feature selection model for software defect prediction using binary Rao optimization algorithm, Appl. Soft Comput., 131, 10.1016/j.asoc.2022.109737
Yu, 2023, Improving effort-aware defect prediction by directly learning to rank software modules, Inf. Softw. Technol., 10.1016/j.infsof.2023.107165
Bennin, 2016, Empirical evaluation of cross-release effort-aware defect prediction models, 214
Ryu, 2016, Effective multi-objective naïve Bayes learning for cross-project defect prediction, Appl. Soft Comput., 49, 1062, 10.1016/j.asoc.2016.04.009
Chen, 2017, Applying feature selection to software defect prediction using multi-objective optimization, 54
Ni, 2019, An empirical study on pareto based multi-objective feature selection for software defect prediction, J. Syst. Softw., 152, 215, 10.1016/j.jss.2019.03.012
Niu, 2018, Adaptive two-SVM multi-objective cuckoo search algorithm for software defect prediction, Int. J. Comput. Sci. Math., 9, 547, 10.1504/IJCSM.2018.096327
Cao, 2018, An improved twin support vector machine based on multi-objective cuckoo search for software defect prediction, Int. J. Bio-Inspired Comput., 11, 282, 10.1504/IJBIC.2018.092808
Cai, 2020, An under-sampled software defect prediction method based on hybrid multi-objective cuckoo search, Concurr. Comput.: Pract. Exper., 32, 10.1002/cpe.5478
Zhang, 2021, WGNCS: A robust hybrid cross-version defect model via multi-objective optimization and deep enhanced feature representation, Inform. Sci., 570, 545, 10.1016/j.ins.2021.05.008
Kanwar, 2023, Efficient random forest algorithm for multi-objective optimization in software defect prediction, IETE J. Res., 1, 10.1080/03772063.2023.2205377
Ye, 2023, A novel multi-objective immune optimization algorithm for under sampling software defect prediction problem, Concurr. Comput.: Pract. Exper., 35, 10.1002/cpe.7525