{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T21:42:47Z","timestamp":1774993367726,"version":"3.50.1"},"reference-count":189,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-024-00716-0","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T17:03:22Z","timestamp":1737651802000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Adaptive Ensemble Learning Model-Based Binary White Shark Optimizer for Software Defect Classification"],"prefix":"10.1007","volume":"18","author":[{"given":"Jameel","family":"Saraireh","sequence":"first","affiliation":[]},{"given":"Mary","family":"Agoyi","sequence":"additional","affiliation":[]},{"given":"Sofian","family":"Kassaymeh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"issue":"2","key":"716_CR1","doi-asserted-by":"publisher","first-page":"103744","DOI":"10.1016\/j.im.2022.103744","volume":"60","author":"S Madhavaram","year":"2023","unstructured":"Madhavaram, S., Appan, R., Manis, K., Browne, G.J.: Building capabilities for software development firm competitiveness: the role of intellectual capital and intra-firm relational capital. Inf. Manag. 60(2), 103744 (2023). https:\/\/doi.org\/10.1016\/j.im.2022.103744","journal-title":"Inf. Manag."},{"issue":"1","key":"716_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1049\/sfw2.12007","volume":"15","author":"KK Holgeid","year":"2021","unstructured":"Holgeid, K.K., Jorgensen, M., Sjoberg, D.I., Krogstie, J.: Benefits management in software development: a systematic review of empirical studies. IET Softw. 15(1), 1\u201324 (2021). https:\/\/doi.org\/10.1049\/sfw2.12007","journal-title":"IET Softw."},{"key":"716_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.dajour.2022.100159","volume":"6","author":"V Singh","year":"2023","unstructured":"Singh, V., Kumar, V., Singh, V.: A hybrid novel fuzzy ahp-topsis technique for selecting parameter-influencing testing in software development. Decis. Anal. J. 6, 100159 (2023). https:\/\/doi.org\/10.1016\/j.dajour.2022.100159","journal-title":"Decis. Anal. J."},{"key":"716_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.scico.2021.102732","volume":"214","author":"C-H Cai","year":"2022","unstructured":"Cai, C.-H., Sun, J., Dobbie, G.: B model quality assessments on automated reachability repair with iso\/iec 25010. Sci. Comput. Program. 214, 102732 (2022). https:\/\/doi.org\/10.1016\/j.scico.2021.102732","journal-title":"Sci. Comput. Program."},{"issue":"6","key":"716_CR5","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1007\/s10664-023-10381-0","volume":"28","author":"J Borstler","year":"2023","unstructured":"Borstler, J., Bennin, K.E., Hooshangi, S., Jeuring, J., Keuning, H., Kleiner, C., MacKellar, B., Duran, R., Storrle, H., Toll, D., et al.: Developers talking about code quality. Empir. Softw. Eng. 28(6), 128 (2023). https:\/\/doi.org\/10.1007\/s10664-023-10381-0","journal-title":"Empir. Softw. Eng."},{"key":"716_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-023-08174-0","author":"J Al Dallal","year":"2023","unstructured":"Al Dallal, J., Abdulsalam, H., AlMarzouq, M., Selamat, A.: Machine learning-based exploration of the impact of move method refactoring on object-oriented software quality attributes. Arab. J. Sci. Eng. (2023). https:\/\/doi.org\/10.1007\/s13369-023-08174-0","journal-title":"Arab. J. Sci. Eng."},{"key":"716_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2020.106449","volume":"131","author":"S Li","year":"2021","unstructured":"Li, S., Zhang, H., Jia, Z., Zhong, C., Zhang, C., Shan, Z., Shen, J., Babar, M.A.: Understanding and addressing quality attributes of microservices architecture: a systematic literature review. Inf. Softw. Technol. 131, 106449 (2021). https:\/\/doi.org\/10.1016\/j.infsof.2020.106449","journal-title":"Inf. Softw. Technol."},{"issue":"7","key":"716_CR8","doi-asserted-by":"publisher","first-page":"5339","DOI":"10.1007\/s00500-020-05532-0","volume":"25","author":"T Yaghoobi","year":"2021","unstructured":"Yaghoobi, T.: Selection of optimal software reliability growth model using a diversity index. Soft. Comput. 25(7), 5339\u20135353 (2021). https:\/\/doi.org\/10.1007\/s00500-020-05532-0","journal-title":"Soft. Comput."},{"issue":"5","key":"716_CR9","doi-asserted-by":"publisher","first-page":"2501","DOI":"10.1002\/qre.3087","volume":"38","author":"P Saxena","year":"2022","unstructured":"Saxena, P., Kumar, V., Ram, M.: A novel critic-topsis approach for optimal selection of software reliability growth model (srgm). Qual. Reliab. Eng. Int. 38(5), 2501\u20132520 (2022). https:\/\/doi.org\/10.1002\/qre.3087","journal-title":"Qual. Reliab. Eng. Int."},{"key":"716_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.111267","volume":"188","author":"Y-S Huang","year":"2022","unstructured":"Huang, Y.-S., Chiu, K.-C., Chen, W.-M.: A software reliability growth model for imperfect debugging. J. Syst. Softw. 188, 111267 (2022). https:\/\/doi.org\/10.1016\/j.jss.2022.111267","journal-title":"J. Syst. Softw."},{"key":"716_CR11","doi-asserted-by":"publisher","DOI":"10.1108\/IJQRM-08-2022-0236","author":"V Verma","year":"2023","unstructured":"Verma, V., Anand, S., Aggarwal, A.G.: Optimal time for management review during testing process: an approach using s-curve two-dimensional software reliability growth model. Int. J. Qual. Reliab. Manag. (2023). https:\/\/doi.org\/10.1108\/IJQRM-08-2022-0236","journal-title":"Int. J. Qual. Reliab. Manag."},{"issue":"3","key":"716_CR12","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1108\/IJQRM-05-2021-0139","volume":"39","author":"S Panwar","year":"2022","unstructured":"Panwar, S., Kumar, V., Kapur, P., Singh, O.: Software reliability prediction and release time management with coverage. Int. J. Qual. Reliab. Manag. 39(3), 741\u2013761 (2022). https:\/\/doi.org\/10.1108\/IJQRM-05-2021-0139","journal-title":"Int. J. Qual. Reliab. Manag."},{"key":"716_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-05347-4_12","author":"V Pradhan","year":"2022","unstructured":"Pradhan, V., Dhar, J., Kumar, A.: Software reliability models and multi-attribute utility function based strategic decision for release time optimization. Predict. Anal. Syst. Reliab. (2022). https:\/\/doi.org\/10.1007\/978-3-031-05347-4_12","journal-title":"Predict. Anal. Syst. Reliab."},{"key":"716_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2023.111853","volume":"206","author":"X Chen","year":"2023","unstructured":"Chen, X., Xia, H., Pei, W., Ni, C., Liu, K.: Boosting multi-objectie just-in-time software defect prediction by fusing expert metrics and semantic metrics. J. Syst. Softw. 206, 111853 (2023). https:\/\/doi.org\/10.1016\/j.jss.2023.111853","journal-title":"J. Syst. Softw."},{"key":"716_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2022.107088","volume":"153","author":"S Hameed","year":"2023","unstructured":"Hameed, S., Elsheikh, Y., Azzeh, M.: An optimized case-based software project effort estimation using genetic algorithm. Inf. Softw. Technol. 153, 107088 (2023). https:\/\/doi.org\/10.1016\/j.infsof.2022.107088","journal-title":"Inf. Softw. Technol."},{"issue":"10","key":"716_CR16","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.1108\/IJQRM-07-2019-0235","volume":"38","author":"AR Pai","year":"2021","unstructured":"Pai, A.R., Joshi, G., Rane, S.: Quality and reliability studies in software defect management: a literature review. Int. J. Qual. Reliab. Manag. 38(10), 2007\u20132033 (2021). https:\/\/doi.org\/10.1108\/IJQRM-07-2019-0235","journal-title":"Int. J. Qual. Reliab. Manag."},{"issue":"3","key":"716_CR17","doi-asserted-by":"publisher","first-page":"2405","DOI":"10.1007\/s13204-021-02204-9","volume":"13","author":"N Sreekanth","year":"2023","unstructured":"Sreekanth, N., Rama Devi, J., Shukla, K.A., Mohanty, D., Srinivas, A., Rao, G.N., Alam, A., Gupta, A.: Evaluation of estimation in software development using deep learning-modified neural network. Appl. Nanosci. 13(3), 2405\u20132417 (2023). https:\/\/doi.org\/10.1007\/s13204-021-02204-9","journal-title":"Appl. Nanosci."},{"key":"716_CR18","doi-asserted-by":"publisher","unstructured":"Ritu, Bhambri, P.: Software effort estimation with machine learning\u2013a systematic literature review. In: Agile software development: trends, challenges and applications, pp. 291\u2013308. Wiley Online Library (2023) https:\/\/doi.org\/10.1002\/9781119896838.ch15","DOI":"10.1002\/9781119896838.ch15"},{"key":"716_CR19","doi-asserted-by":"publisher","unstructured":"Manoj, N., Deepak, G.: Sdpo: An approach towards software defect prediction using ontology driven intelligence. In: International Conference on Electrical and Electronics Engineering, pp. 164\u2013172. Springer (2022). https:\/\/doi.org\/10.1007\/978-981-19-1677-9_15","DOI":"10.1007\/978-981-19-1677-9_15"},{"key":"716_CR20","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.neucom.2019.11.067","volume":"385","author":"L Qiao","year":"2020","unstructured":"Qiao, L., Li, X., Umer, Q., Guo, P.: Deep learning based software defect prediction. Neurocomputing 385, 100\u2013110 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2019.11.067","journal-title":"Neurocomputing"},{"key":"716_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119625","volume":"218","author":"S Kanwar","year":"2023","unstructured":"Kanwar, S., Awasthi, L.K., Shrivastava, V.: Candidate project selection in cross project defect prediction using hybrid method. Expert Syst. Appl. 218, 119625 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.119625","journal-title":"Expert Syst. Appl."},{"key":"716_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.111537","volume":"195","author":"G Giray","year":"2023","unstructured":"Giray, G., Bennin, K.E., Koksal, O., Babur, O., Tekinerdogan, B.: On the use of deep learning in software defect prediction. J. Syst. Softw. 195, 111537 (2023). https:\/\/doi.org\/10.1016\/j.jss.2022.111537","journal-title":"J. Syst. Softw."},{"issue":"Suppl 1","key":"716_CR23","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s13198-022-01831-x","volume":"14","author":"Y Khatri","year":"2023","unstructured":"Khatri, Y., Singh, S.K.: An effective feature selection based cross-project defect prediction model for software quality improvement. Int. J. Syst. Assur. Eng. Manag. 14(Suppl 1), 154\u2013172 (2023). https:\/\/doi.org\/10.1007\/s13198-022-01831-x","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"716_CR24","doi-asserted-by":"crossref","unstructured":"Jing, X.-Y., Chen, H., Xu, B.: Cross-project defect prediction. In: Intelligent Software Defect Prediction, pp. 35\u201363. Springer (2024)","DOI":"10.1007\/978-981-99-2842-2_4"},{"key":"716_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.111245","volume":"188","author":"W Zheng","year":"2022","unstructured":"Zheng, W., Shen, T., Chen, X., Deng, P.: Interpretability application of the just-in-time software defect prediction model. J. Syst. Softw. 188, 111245 (2022). https:\/\/doi.org\/10.1016\/j.jss.2022.111245","journal-title":"J. Syst. Softw."},{"issue":"2","key":"716_CR26","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s13198-021-01326-1","volume":"13","author":"S Goyal","year":"2022","unstructured":"Goyal, S.: Effective software defect prediction using support vector machines (svms). Int. J. Syst. Assur. Eng. Manag. 13(2), 681\u2013696 (2022). https:\/\/doi.org\/10.1007\/s13198-021-01326-1","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"716_CR27","doi-asserted-by":"publisher","unstructured":"Awotunde, J.B., Misra, S., Adeniyi, A.E., Abiodun, M.K., Kaushik, M., Lawrence, M.O.: A feature selection-based k-nn model for fast software defect prediction. In: International Conference on Computational Science and Its Applications, pp. 49\u201361. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-10542-5_4","DOI":"10.1007\/978-3-031-10542-5_4"},{"key":"716_CR28","doi-asserted-by":"publisher","unstructured":"Cetiner, M., Sahingoz, O.K.: A comparative analysis for machine learning based software defect prediction systems. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1\u20137. IEEE (2020).https:\/\/doi.org\/10.1109\/ICCCNT49239.2020.9225352","DOI":"10.1109\/ICCCNT49239.2020.9225352"},{"key":"716_CR29","doi-asserted-by":"publisher","first-page":"4714","DOI":"10.1016\/j.matpr.2022.03.165","volume":"62","author":"F Alaswad","year":"2022","unstructured":"Alaswad, F., Poovammal, E.: Software quality prediction using machine learning. Mater. Today: Proc. 62, 4714\u20134720 (2022). https:\/\/doi.org\/10.1016\/j.matpr.2022.03.165","journal-title":"Mater. Today: Proc."},{"key":"716_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-023-04170-z","author":"NAA Khleel","year":"2023","unstructured":"Khleel, N.A.A., Nehez, K.: Software defect prediction using a bidirectional lstm network combined with oversampling techniques. Clust. Comput. (2023). https:\/\/doi.org\/10.1007\/s10586-023-04170-z","journal-title":"Clust. Comput."},{"key":"716_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-023-07984-6","author":"J Liu","year":"2023","unstructured":"Liu, J., Lei, J., Liao, Z., He, J.: Software defect prediction model based on improved twin support vector machines. Soft Comput. (2023). https:\/\/doi.org\/10.1007\/s00500-023-07984-6","journal-title":"Soft Comput."},{"issue":"1","key":"716_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.jer.2023.100002","volume":"11","author":"R Chennappan","year":"2023","unstructured":"Chennappan, R., et al.: An automated software failure prediction technique using hybrid machine learning algorithms. J. Eng. Res. 11(1), 100002 (2023). https:\/\/doi.org\/10.1016\/j.jer.2023.100002","journal-title":"J. Eng. Res."},{"issue":"3","key":"716_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3564821","volume":"32","author":"A Di Sorbo","year":"2023","unstructured":"Di Sorbo, A., Zampetti, F., Visaggio, A., Di Penta, M., Panichella, S.: Automated identification and qualitative characterization of safety concerns reported in uav software platforms. ACM Trans. Softw. Eng. Methodol. 32(3), 1\u201337 (2023). https:\/\/doi.org\/10.1145\/3564821","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"716_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2020.106287","volume":"122","author":"N Li","year":"2020","unstructured":"Li, N., Shepperd, M., Guo, Y.: A systematic review of unsupervised learning techniques for software defect prediction. Inf. Softw. Technol. 122, 106287 (2020). https:\/\/doi.org\/10.1016\/j.infsof.2020.106287","journal-title":"Inf. Softw. Technol."},{"key":"716_CR35","doi-asserted-by":"publisher","unstructured":"Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., Aljaaf, A.J.: A systematic review on supervised and unsupervised machine learning algorithms for data science. In: Supervised and unsupervised learning for data science, pp. 3\u201321. Springer (2020). https:\/\/doi.org\/10.1007\/978-3-030-22475-2_1","DOI":"10.1007\/978-3-030-22475-2_1"},{"issue":"5","key":"716_CR36","doi-asserted-by":"publisher","first-page":"85","DOI":"10.4236\/jsea.2019.125007","volume":"12","author":"A Alsaeedi","year":"2019","unstructured":"Alsaeedi, A., Khan, M.Z.: Software defect prediction using supervised machine learning and ensemble techniques: a comparative study. J. Softw. Eng. Appl. 12(5), 85\u2013100 (2019). https:\/\/doi.org\/10.4236\/jsea.2019.125007","journal-title":"J. Softw. Eng. Appl."},{"issue":"6","key":"716_CR37","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1007\/s12530-022-09423-7","volume":"13","author":"SK Pemmada","year":"2022","unstructured":"Pemmada, S.K., Behera, H., Nayak, J., Naik, B.: Correlation-based modified long short-term memory network approach for software defect prediction. Evol. Syst. 13(6), 869\u2013887 (2022). https:\/\/doi.org\/10.1007\/s12530-022-09423-7","journal-title":"Evol. Syst."},{"key":"716_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102217","author":"K Bayoudh","year":"2023","unstructured":"Bayoudh, K.: A survey of multimodal hybrid deep learning for computer vision: architectures, applications, trends, and challenges. Inf. Fusion (2023). https:\/\/doi.org\/10.1016\/j.inffus.2023.102217","journal-title":"Inf. Fusion"},{"issue":"2","key":"716_CR39","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1007\/s10515-022-00344-y","volume":"29","author":"M Mahdieh","year":"2022","unstructured":"Mahdieh, M., Mirian-Hosseinabadi, S.-H., Mahdieh, M.: Test case prioritization using test case diversification and fault-proneness estimations. Autom. Softw. Eng. 29(2), 50 (2022). https:\/\/doi.org\/10.1007\/s10515-022-00344-y","journal-title":"Autom. Softw. Eng."},{"key":"716_CR40","doi-asserted-by":"publisher","unstructured":"Shah, M., Kantawala, H., Gandhi, K., Patel, R., Patel, K.A., Kothari, A.: Theoretical evaluation of ensemble machine learning techniques. In: 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 829\u2013837. IEEE (2023). https:\/\/doi.org\/10.1109\/ICSSIT55814.2023.10061139","DOI":"10.1109\/ICSSIT55814.2023.10061139"},{"key":"716_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2023.01.014","author":"A Mohammed","year":"2023","unstructured":"Mohammed, A., Kora, R.: A comprehensive review on ensemble deep learning: opportunities and challenges. J. King Saud Univ. Comput. Inf. Sci. (2023). https:\/\/doi.org\/10.1016\/j.jksuci.2023.01.014","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"issue":"31","key":"716_CR42","doi-asserted-by":"publisher","first-page":"23103","DOI":"10.1007\/s00521-023-08957-4","volume":"35","author":"T Talaei Khoei","year":"2023","unstructured":"Talaei Khoei, T., Ould Slimane, H., Kaabouch, N.: Deep learning: systematic review, models, challenges, and research directions. Neural Comput. Appl. 35(31), 23103\u201323124 (2023). https:\/\/doi.org\/10.1007\/s00521-023-08957-4","journal-title":"Neural Comput. Appl."},{"key":"716_CR43","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.comcom.2024.01.018","volume":"216","author":"Z Abou El Houda","year":"2024","unstructured":"Abou El Houda, Z., Brik, B., Ksentini, A.: Securing iiot applications in 6g and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning. Comput. Commun. 216, 260\u2013273 (2024). https:\/\/doi.org\/10.1016\/j.comcom.2024.01.018","journal-title":"Comput. Commun."},{"key":"716_CR44","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/j.neucom.2022.05.080","volume":"500","author":"T Batista","year":"2022","unstructured":"Batista, T., Bedregal, B., Moraes, R.: Constructing multi-layer classifier ensembles using the choquet integral based on overlap and quasi-overlap functions. Neurocomputing 500, 413\u2013421 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2022.05.080","journal-title":"Neurocomputing"},{"key":"716_CR45","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.ijcce.2021.01.001","volume":"2","author":"S Kumari","year":"2021","unstructured":"Kumari, S., Kumar, D., Mittal, M.: An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. Int. J. Cogn. Comput. Eng. 2, 40\u201346 (2021). https:\/\/doi.org\/10.1016\/j.ijcce.2021.01.001","journal-title":"Int. J. Cogn. Comput. Eng."},{"key":"716_CR46","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1016\/j.psep.2022.11.073","volume":"169","author":"MG Uddin","year":"2023","unstructured":"Uddin, M.G., Nash, S., Rahman, A., Olbert, A.I.: Performance analysis of the water quality index model for predicting water state using machine learning techniques. Process Saf. Environ. Prot. 169, 808\u2013828 (2023). https:\/\/doi.org\/10.1016\/j.psep.2022.11.073","journal-title":"Process Saf. Environ. Prot."},{"issue":"12","key":"716_CR47","doi-asserted-by":"publisher","first-page":"0295234","DOI":"10.1371\/journal.pone.0295234","volume":"18","author":"SM Ganie","year":"2023","unstructured":"Ganie, S.M., Dutta Pramanik, P.K., Mallik, S., Zhao, Z.: Chronic kidney disease prediction using boosting techniques based on clinical parameters. PLoS ONE 18(12), 0295234 (2023). https:\/\/doi.org\/10.1371\/journal.pone.0295234","journal-title":"PLoS ONE"},{"issue":"15","key":"716_CR48","doi-asserted-by":"publisher","first-page":"18715","DOI":"10.1007\/s10489-022-04427-x","volume":"53","author":"M Mafarja","year":"2023","unstructured":"Mafarja, M., Thaher, T., Al-Betar, M.A., Too, J., Awadallah, M.A., Abu Doush, I., Turabieh, H.: Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning. Appl. Intell. 53(15), 18715\u201318757 (2023). https:\/\/doi.org\/10.1007\/s10489-022-04427-x","journal-title":"Appl. Intell."},{"key":"716_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.108923","volume":"111","author":"M Al-Laham","year":"2023","unstructured":"Al-Laham, M., Kassaymeh, S., Al-Betar, M.A., Makhadmeh, S.N., Albashish, D., Alweshah, M.: An efficient convergence-boosted salp swarm optimizer-based artificial neural network for the development of software fault prediction models. Comput. Electr. Eng. 111, 108923 (2023). https:\/\/doi.org\/10.1016\/j.compeleceng.2023.108923","journal-title":"Comput. Electr. Eng."},{"issue":"6","key":"716_CR50","doi-asserted-by":"publisher","first-page":"1967","DOI":"10.1007\/s13042-022-01740-2","volume":"14","author":"Y Tang","year":"2023","unstructured":"Tang, Y., Dai, Q., Yang, M., Du, T., Chen, L.: Software defect prediction ensemble learning algorithm based on adaptive variable sparrow search algorithm. Int. J. Mach. Learn. Cybern. 14(6), 1967\u20131987 (2023). https:\/\/doi.org\/10.1007\/s13042-022-01740-2","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"12","key":"716_CR51","doi-asserted-by":"publisher","first-page":"8789","DOI":"10.1080\/03772063.2022.2069603","volume":"69","author":"L Raamesh","year":"2023","unstructured":"Raamesh, L., Jothi, S., Radhika, S.: Enhancing software reliability and fault detection using hybrid brainstorm optimization-based lstm model. IETE J. Res. 69(12), 8789\u20138803 (2023). https:\/\/doi.org\/10.1080\/03772063.2022.2069603","journal-title":"IETE J. Res."},{"key":"716_CR52","doi-asserted-by":"publisher","unstructured":"Mondal, S., Sahu, A.K., Kumar, H., Pattanayak, R.M., Gourisaria, M.K., Das, H.: Software fault prediction using wrapper based ant colony optimization algorithm for feature selection. In: 2023 6th International Conference on Information Systems and Computer Networks (ISCON), pp. 1\u20136. IEEE (2023). https:\/\/doi.org\/10.1109\/ISCON57294.2023.10111995","DOI":"10.1109\/ISCON57294.2023.10111995"},{"key":"716_CR53","doi-asserted-by":"publisher","unstructured":"Pandit, M.B.R., Varma, N.: A deep introduction to ai based software defect prediction (sdp) and its current challenges. In: TENCON 2019-2019 IEEE Region 10 Conference (TENCON), pp. 284\u2013290. IEEE (2019). https:\/\/doi.org\/10.1109\/TENCON.2019.8929661","DOI":"10.1109\/TENCON.2019.8929661"},{"issue":"3","key":"716_CR54","doi-asserted-by":"publisher","first-page":"2581","DOI":"10.1007\/s10586-021-03282-8","volume":"24","author":"M Mustaqeem","year":"2021","unstructured":"Mustaqeem, M., Saqib, M.: Principal component based support vector machine (pc-svm): a hybrid technique for software defect detection. Clust. Comput. 24(3), 2581\u20132595 (2021). https:\/\/doi.org\/10.1007\/s10586-021-03282-8","journal-title":"Clust. Comput."},{"key":"716_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114637","volume":"171","author":"C Jin","year":"2021","unstructured":"Jin, C.: Cross-project software defect prediction based on domain adaptation learning and optimization. Expert Syst. Appl. 171, 114637 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2021.114637","journal-title":"Expert Syst. Appl."},{"key":"716_CR56","doi-asserted-by":"publisher","first-page":"114629","DOI":"10.1109\/ACCESS.2022.3217480","volume":"10","author":"F Alghanim","year":"2022","unstructured":"Alghanim, F., Azzeh, M., El-Hassan, A., Qattous, H.: Software defect density prediction using deep learning. IEEE Access 10, 114629\u2013114641 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3217480","journal-title":"IEEE Access"},{"issue":"3","key":"716_CR57","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1007\/s10462-021-10044-w","volume":"55","author":"S Goyal","year":"2022","unstructured":"Goyal, S.: Handling class-imbalance with knn (neighbourhood) under-sampling for software defect prediction. Artif. Intell. Rev. 55(3), 2023\u20132064 (2022). https:\/\/doi.org\/10.1007\/s10462-021-10044-w","journal-title":"Artif. Intell. Rev."},{"issue":"5","key":"716_CR58","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.3390\/app10051745","volume":"10","author":"H Alsawalqah","year":"2020","unstructured":"Alsawalqah, H., Hijazi, N., Eshtay, M., Faris, H., Radaideh, A.A., Aljarah, I., Alshamaileh, Y.: Software defect prediction using heterogeneous ensemble classification based on segmented patterns. Appl. Sci. 10(5), 1745 (2020). https:\/\/doi.org\/10.3390\/app10051745","journal-title":"Appl. Sci."},{"key":"716_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2021.106664","volume":"139","author":"J Yao","year":"2021","unstructured":"Yao, J., Shepperd, M.: The impact of using biased performance metrics on software defect prediction research. Inf. Softw. Technol. 139, 106664 (2021). https:\/\/doi.org\/10.1016\/j.infsof.2021.106664","journal-title":"Inf. Softw. Technol."},{"key":"716_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2022.106847","volume":"146","author":"X Yu","year":"2022","unstructured":"Yu, X., Keung, J., Xiao, Y., Feng, S., Li, F., Dai, H.: Predicting the precise number of software defects: are we there yet? Inf. Softw. Technol. 146, 106847 (2022). https:\/\/doi.org\/10.1016\/j.infsof.2022.106847","journal-title":"Inf. Softw. Technol."},{"key":"716_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107886","volume":"100","author":"I Batool","year":"2022","unstructured":"Batool, I., Khan, T.A.: Software fault prediction using data mining, machine learning and deep learning techniques: a systematic literature review. Comput. Electr. Eng. 100, 107886 (2022). https:\/\/doi.org\/10.1016\/j.compeleceng.2022.107886","journal-title":"Comput. Electr. Eng."},{"key":"716_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2021.106530","volume":"133","author":"X Wu","year":"2021","unstructured":"Wu, X., Zheng, W., Chen, X., Zhao, Y., Yu, T., Mu, D.: Improving high-impact bug report prediction with combination of interactive machine learning and active learning. Inf. Softw. Technol. 133, 106530 (2021). https:\/\/doi.org\/10.1016\/j.infsof.2021.106530","journal-title":"Inf. Softw. Technol."},{"key":"716_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2021.111031","volume":"180","author":"G Giray","year":"2021","unstructured":"Giray, G.: A software engineering perspective on engineering machine learning systems: state of the art and challenges. J. Syst. Softw. 180, 111031 (2021). https:\/\/doi.org\/10.1016\/j.jss.2021.111031","journal-title":"J. Syst. Softw."},{"key":"716_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.matcom.2023.11.019","author":"EH Houssein","year":"2023","unstructured":"Houssein, E.H., Saeed, M.K., Al-Sayed, M.M.: Ewso: boosting white shark optimizer for solving engineering design and combinatorial problems. Math. Comput. Simul. (2023). https:\/\/doi.org\/10.1016\/j.matcom.2023.11.019","journal-title":"Math. Comput. Simul."},{"key":"716_CR65","doi-asserted-by":"publisher","first-page":"108457","DOI":"10.1016\/j.knosys.2022.108457","volume":"243","author":"M Braik","year":"2022","unstructured":"Braik, M., Hammouri, A., Atwan, J., Al-Betar, M.A., Awadallah, M.A.: White shark optimizer: a novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowl. Based Syst. 243, 108457 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.108457","journal-title":"Knowl. Based Syst."},{"key":"716_CR66","doi-asserted-by":"publisher","first-page":"12201","DOI":"10.1007\/s00521-019-04368-6","volume":"32","author":"H Chantar","year":"2020","unstructured":"Chantar, H., Mafarja, M., Alsawalqah, H., Heidari, A.A., Aljarah, I., Faris, H.: Feature selection using binary grey wolf optimizer with elite-based crossover for Arabic text classification. Neural Comput. Appl. 32, 12201\u201312220 (2020). https:\/\/doi.org\/10.1007\/s00521-019-04368-6","journal-title":"Neural Comput. Appl."},{"issue":"10","key":"716_CR67","doi-asserted-by":"publisher","first-page":"6049","DOI":"10.3390\/su14106049","volume":"14","author":"MA Ali","year":"2022","unstructured":"Ali, M.A., Kamel, S., Hassan, M.H., Ahmed, E.M., Alanazi, M.: Optimal power flow solution of power systems with renewable energy sources using white sharks algorithm. Sustainability 14(10), 6049 (2022). https:\/\/doi.org\/10.3390\/su14106049","journal-title":"Sustainability"},{"key":"716_CR68","doi-asserted-by":"publisher","first-page":"132212","DOI":"10.1109\/ACCESS.2022.3229434","volume":"10","author":"SN Makhadmeh","year":"2022","unstructured":"Makhadmeh, S.N., Al-Betar, M.A., Assaleh, K., Kassaymeh, S.: A hybrid white shark equilibrium optimizer for power scheduling problem based iot. IEEE Access 10, 132212\u2013132231 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3229434","journal-title":"IEEE Access"},{"key":"716_CR69","doi-asserted-by":"publisher","unstructured":"Zhang, R., Li, X., Ding, Y., Ren, H.: Uav path planning method based on modified white shark optimization. In: 2022 IEEE International Conference on Unmanned Systems (ICUS), pp. 380\u2013386. IEEE (2022). https:\/\/doi.org\/10.1109\/ICUS55513.2022.9987109","DOI":"10.1109\/ICUS55513.2022.9987109"},{"issue":"7","key":"716_CR70","doi-asserted-by":"publisher","first-page":"1386","DOI":"10.3390\/jmse11071386","volume":"11","author":"J Liang","year":"2023","unstructured":"Liang, J., Liu, L.: Optimal path planning method for unmanned surface vehicles based on improved shark-inspired algorithm. J. Mar. Sci. Eng. 11(7), 1386 (2023). https:\/\/doi.org\/10.3390\/jmse11071386","journal-title":"J. Mar. Sci. Eng."},{"issue":"7","key":"716_CR71","doi-asserted-by":"publisher","first-page":"5667","DOI":"10.3390\/su15075667","volume":"15","author":"A Fathy","year":"2023","unstructured":"Fathy, A., Yousri, D., Alharbi, A.G., Abdelkareem, M.A.: A new hybrid white shark and whale optimization approach for estimating the li-ion battery model parameters. Sustainability 15(7), 5667 (2023). https:\/\/doi.org\/10.3390\/su15075667","journal-title":"Sustainability"},{"key":"716_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121474","volume":"237","author":"N Amor","year":"2024","unstructured":"Amor, N., Noman, M.T., Petru, M., Sebastian, N., Balram, D.: Design and optimization of machinability of zno embedded-glass fiber reinforced polymer composites with a modified white shark optimizer. Expert Syst. Appl. 237, 121474 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.121474","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"716_CR73","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s42979-023-02341-8","volume":"5","author":"S Supreeth","year":"2023","unstructured":"Supreeth, S., Bhargavi, S., Margam, R., Annaiah, H., Nandalike, R.: Virtual machine placement using Adam white shark optimization algorithm in cloud computing. SN Comput. Sci. 5(1), 21 (2023). https:\/\/doi.org\/10.1007\/s42979-023-02341-8","journal-title":"SN Comput. Sci."},{"key":"716_CR74","doi-asserted-by":"publisher","DOI":"10.1002\/oca.2984","author":"M Lakshmanan","year":"2023","unstructured":"Lakshmanan, M., Kumar, C., Jasper, J.S.: Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell\/module using an improved white shark optimization algorithm. Optim. Control Appl. Methods (2023). https:\/\/doi.org\/10.1002\/oca.2984","journal-title":"Optim. Control Appl. Methods"},{"issue":"3","key":"716_CR75","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/s10844-023-00793-1","volume":"60","author":"NAA Khleel","year":"2023","unstructured":"Khleel, N.A.A., Nehez, K.: A novel approach for software defect prediction using cnn and gru based on smote tomek method. J. Intell. Inf. Syst. 60(3), 673\u2013707 (2023). https:\/\/doi.org\/10.1007\/s10844-023-00793-1","journal-title":"J. Intell. Inf. Syst."},{"key":"716_CR76","doi-asserted-by":"publisher","first-page":"3615","DOI":"10.1007\/s10489-020-01935-6","volume":"51","author":"SS Rathore","year":"2021","unstructured":"Rathore, S.S., Kumar, S.: An empirical study of ensemble techniques for software fault prediction. Appl. Intell. 51, 3615\u20133644 (2021). https:\/\/doi.org\/10.1007\/s10489-020-01935-6","journal-title":"Appl. Intell."},{"key":"716_CR77","doi-asserted-by":"publisher","first-page":"8249","DOI":"10.1007\/s00521-020-04960-1","volume":"33","author":"K Wang","year":"2021","unstructured":"Wang, K., Liu, L., Yuan, C., Wang, Z.: Software defect prediction model based on lasso-svm. Neural Comput. Appl. 33, 8249\u20138259 (2021). https:\/\/doi.org\/10.1007\/s00521-020-04960-1","journal-title":"Neural Comput. Appl."},{"issue":"2","key":"716_CR78","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/s10515-021-00285-y","volume":"28","author":"S Goyal","year":"2021","unstructured":"Goyal, S.: Predicting the defects using stacked ensemble learner with filtered dataset. Autom. Softw. Eng. 28(2), 14 (2021). https:\/\/doi.org\/10.1007\/s10515-021-00285-y","journal-title":"Autom. Softw. Eng."},{"issue":"5","key":"716_CR79","doi-asserted-by":"publisher","first-page":"29","DOI":"10.5815\/ijmecs.2020.05.03","volume":"12","author":"U Ali","year":"2020","unstructured":"Ali, U., Aftab, S., Iqbal, A., Nawaz, Z., Bashir, M.S., Saeed, M.A.: Software defect prediction using variant based ensemble learning and feature selection techniques. Int. J. Mod. Educ. Comput. Sci 12(5), 29\u201340 (2020). https:\/\/doi.org\/10.5815\/ijmecs.2020.05.03","journal-title":"Int. J. Mod. Educ. Comput. Sci"},{"issue":"4","key":"716_CR80","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s11334-021-00399-2","volume":"17","author":"P Suresh Kumar","year":"2021","unstructured":"Suresh Kumar, P., Behera, H.S., Nayak, J., Naik, B.: Bootstrap aggregation ensemble learning-based reliable approach for software defect prediction by using characterized code feature. Innov. Syst. Softw. Eng. 17(4), 355\u2013379 (2021). https:\/\/doi.org\/10.1007\/s11334-021-00399-2","journal-title":"Innov. Syst. Softw. Eng."},{"issue":"9","key":"716_CR81","doi-asserted-by":"publisher","first-page":"4577","DOI":"10.3390\/app12094577","volume":"12","author":"A Alazba","year":"2022","unstructured":"Alazba, A., Aljamaan, H.: Software defect prediction using stacking generalization of optimized tree-based ensembles. Appl. Sci. 12(9), 4577 (2022). https:\/\/doi.org\/10.3390\/app12094577","journal-title":"Appl. Sci."},{"key":"716_CR82","doi-asserted-by":"publisher","unstructured":"Jacob, R.J., Kamat, R.J., Sahithya, N., John, S.S., Shankar, S.P.: Voting based ensemble classification for software defect prediction. In: 2021 IEEE Mysore Sub Section International Conference (MysuruCon), pp. 358\u2013365. IEEE (2021). https:\/\/doi.org\/10.1109\/MysuruCon52639.2021.9641713","DOI":"10.1109\/MysuruCon52639.2021.9641713"},{"issue":"10","key":"716_CR83","doi-asserted-by":"publisher","first-page":"8675","DOI":"10.1016\/j.jksuci.2021.09.010","volume":"34","author":"US Bhutamapuram","year":"2022","unstructured":"Bhutamapuram, U.S., Sadam, R.: With-in-project defect prediction using bootstrap aggregation based diverse ensemble learning technique. J. King Saud Univ. Comput. Inf. Sci. 34(10), 8675\u20138691 (2022). https:\/\/doi.org\/10.1016\/j.jksuci.2021.09.010","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"issue":"2","key":"716_CR84","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s11334-021-00390-x","volume":"18","author":"M Mangla","year":"2022","unstructured":"Mangla, M., Sharma, N., Mohanty, S.N.: A sequential ensemble model for software fault prediction. Innov. Syst. Softw. Eng. 18(2), 301\u2013308 (2022). https:\/\/doi.org\/10.1007\/s11334-021-00390-x","journal-title":"Innov. Syst. Softw. Eng."},{"key":"716_CR85","doi-asserted-by":"publisher","first-page":"86855","DOI":"10.1109\/ACCESS.2021.3072682","volume":"9","author":"J Zheng","year":"2021","unstructured":"Zheng, J., Wang, X., Wei, D., Chen, B., Shao, Y.: A novel imbalanced ensemble learning in software defect predication. IEEE Access 9, 86855\u201386868 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3072682","journal-title":"IEEE Access"},{"key":"716_CR86","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3288156","author":"SA Alsaedi","year":"2023","unstructured":"Alsaedi, S.A., Noaman, A.Y., Gad-Elrab, A.A., Eassa, F.E.: Nature-based prediction model of bug reports based on ensemble machine learning model. IEEE Access (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3288156","journal-title":"IEEE Access"},{"key":"716_CR87","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2024.3356515","author":"J Chen","year":"2024","unstructured":"Chen, J., Xu, J., Cai, S., Wang, X., Chen, H., Li, Z.: Software defect prediction approach based on a diversity ensemble combined with neural network. IEEE Trans. Reliab. (2024). https:\/\/doi.org\/10.1109\/TR.2024.3356515","journal-title":"IEEE Trans. Reliab."},{"key":"716_CR88","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3358201","author":"M Ali","year":"2024","unstructured":"Ali, M., Mazhar, T., Arif, Y., Al-Otaibi, S., Ghadi, Y.Y., Shahzad, T., Khan, M.A., Hamam, H.: Software defect prediction using an intelligent ensemble-based model. IEEE Access (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3358201","journal-title":"IEEE Access"},{"key":"716_CR89","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.111048","volume":"150","author":"RR Panda","year":"2024","unstructured":"Panda, R.R., Nagwani, N.K.: Software bug severity and priority prediction using smote and intuitionistic fuzzy similarity measure. Appl. Soft Comput. 150, 111048 (2024). https:\/\/doi.org\/10.1016\/j.asoc.2023.111048","journal-title":"Appl. Soft Comput."},{"key":"716_CR90","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.matpr.2022.11.229","volume":"81","author":"AA Ceran","year":"2023","unstructured":"Ceran, A.A., Ar, Y., Tanriover, O., Ceran, S.S.: Prediction of software quality with machine learning-based ensemble methods. Mater. Today: Proc. 81, 18\u201325 (2023). https:\/\/doi.org\/10.1016\/j.matpr.2022.11.229","journal-title":"Mater. Today: Proc."},{"issue":"1","key":"716_CR91","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s11334-022-00506-x","volume":"19","author":"F Johnson","year":"2023","unstructured":"Johnson, F., Oluwatobi, O., Folorunso, O., Ojumu, A.V., Quadri, A.: Optimized ensemble machine learning model for software bugs prediction. Innov. Syst. Softw. Eng. 19(1), 91\u2013101 (2023). https:\/\/doi.org\/10.1007\/s11334-022-00506-x","journal-title":"Innov. Syst. Softw. Eng."},{"issue":"3","key":"716_CR92","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1007\/s13198-023-01902-7","volume":"14","author":"K Bansal","year":"2023","unstructured":"Bansal, K., Singh, G., Malik, S., Rohil, H.: Nrpredictor: an ensemble learning and feature selection based approach for predicting the non-reproducible bugs. Int. J. Syst. Assur. Eng. Manag. 14(3), 989\u20131009 (2023). https:\/\/doi.org\/10.1007\/s13198-023-01902-7","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"716_CR93","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1016\/j.ins.2022.07.130","volume":"609","author":"F Jiang","year":"2022","unstructured":"Jiang, F., Yu, X., Gong, D., Du, J.: A random approximate reduct-based ensemble learning approach and its application in software defect prediction. Inf. Sci. 609, 1147\u20131168 (2022). https:\/\/doi.org\/10.1016\/j.ins.2022.07.130","journal-title":"Inf. Sci."},{"issue":"4","key":"716_CR94","doi-asserted-by":"publisher","first-page":"3333","DOI":"10.1007\/s40747-022-00676-y","volume":"8","author":"L Chen","year":"2022","unstructured":"Chen, L., Wang, C., Song, S.-L.: Software defect prediction based on nested-stacking and heterogeneous feature selection. Complex Intell. Syst. 8(4), 3333\u20133348 (2022). https:\/\/doi.org\/10.1007\/s40747-022-00676-y","journal-title":"Complex Intell. Syst."},{"key":"716_CR95","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119015","volume":"213","author":"EH Houssein","year":"2023","unstructured":"Houssein, E.H., Oliva, D., Celik, E., Emam, M.M., Ghoniem, R.M.: Boosted sooty tern optimization algorithm for global optimization and feature selection. Expert Syst. Appl. 213, 119015 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2022.119015","journal-title":"Expert Syst. Appl."},{"key":"716_CR96","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.neucom.2022.04.083","volume":"494","author":"T Dokeroglu","year":"2022","unstructured":"Dokeroglu, T., Deniz, A., Kiziloz, H.E.: A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing 494, 269\u2013296 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2022.04.083","journal-title":"Neurocomputing"},{"issue":"2","key":"716_CR97","doi-asserted-by":"publisher","first-page":"1749","DOI":"10.1007\/s00521-022-07836-8","volume":"35","author":"M Mafarja","year":"2023","unstructured":"Mafarja, M., Thaher, T., Too, J., Chantar, H., Turabieh, H., Houssein, E.H., Emam, M.M.: An efficient high-dimensional feature selection approach driven by enhanced multi-strategy grey wolf optimizer for biological data classification. Neural Comput. Appl. 35(2), 1749\u20131775 (2023). https:\/\/doi.org\/10.1007\/s00521-022-07836-8","journal-title":"Neural Comput. Appl."},{"issue":"24","key":"716_CR98","doi-asserted-by":"publisher","first-page":"16899","DOI":"10.1007\/s00521-021-06273-3","volume":"33","author":"EH Houssein","year":"2021","unstructured":"Houssein, E.H., Emam, M.M., Ali, A.A.: Improved manta ray foraging optimization for multi-level thresholding using covid-19 ct images. Neural Comput. Appl. 33(24), 16899\u201316919 (2021). https:\/\/doi.org\/10.1007\/s00521-021-06273-3","journal-title":"Neural Comput. Appl."},{"issue":"7","key":"716_CR99","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.3390\/ph16071050","volume":"16","author":"MA Raslan","year":"2023","unstructured":"Raslan, M.A., Raslan, S.A., Shehata, E.M., Mahmoud, A.S., Sabri, N.A.: Advances in the applications of bioinformatics and chemoinformatics. Pharmaceuticals 16(7), 1050 (2023). https:\/\/doi.org\/10.3390\/ph16071050","journal-title":"Pharmaceuticals"},{"key":"716_CR100","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104827","volume":"137","author":"M Tougaccar","year":"2021","unstructured":"Tougaccar, M.: Disease type detection in lung and colon cancer images using the complement approach of inefficient sets. Comput. Biol. Med. 137, 104827 (2021). https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104827","journal-title":"Comput. Biol. Med."},{"key":"716_CR101","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110659","volume":"146","author":"T Zivkovic","year":"2023","unstructured":"Zivkovic, T., Nikolic, B., Simic, V., Pamucar, D., Bacanin, N.: Software defects prediction by metaheuristics tuned extreme gradient boosting and analysis based on Shapley additive explanations. Appl. Soft Comput. 146, 110659 (2023). https:\/\/doi.org\/10.1016\/j.asoc.2023.110659","journal-title":"Appl. Soft Comput."},{"issue":"04","key":"716_CR102","doi-asserted-by":"publisher","first-page":"2450045","DOI":"10.1142\/S0219467824500451","volume":"24","author":"M Prashanthi","year":"2024","unstructured":"Prashanthi, M., Chandra Mohan, M.: Hybrid optimization-based neural network classifier for software defect prediction. Int. J. Image Graph. 24(04), 2450045 (2024). https:\/\/doi.org\/10.1142\/S0219467824500451","journal-title":"Int. J. Image Graph."},{"key":"716_CR103","doi-asserted-by":"publisher","first-page":"2185","DOI":"10.1007\/s41870-021-00804-w","volume":"13","author":"S Goyal","year":"2021","unstructured":"Goyal, S., Bhatia, P.K.: Software fault prediction using lion optimization algorithm. Int. J. Inf. Technol. 13, 2185\u20132190 (2021). https:\/\/doi.org\/10.1007\/s41870-021-00804-w","journal-title":"Int. J. Inf. Technol."},{"issue":"4","key":"716_CR104","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1049\/sfw2.12091","volume":"17","author":"M Shafiq","year":"2023","unstructured":"Shafiq, M., Alghamedy, F.H., Jamal, N., Kamal, T., Daradkeh, Y.I., Shabaz, M.: Retracted: scientific programming using optimized machine learning techniques for software fault prediction to improve software quality. IET Softw. 17(4), 694\u2013704 (2023). https:\/\/doi.org\/10.1049\/sfw2.12091","journal-title":"IET Softw."},{"issue":"11","key":"716_CR105","doi-asserted-by":"publisher","first-page":"2438","DOI":"10.3390\/math11112438","volume":"11","author":"H Das","year":"2023","unstructured":"Das, H., Prajapati, S., Gourisaria, M.K., Pattanayak, R.M., Alameen, A., Kolhar, M.: Feature selection using golden jackal optimization for software fault prediction. Mathematics 11(11), 2438 (2023). https:\/\/doi.org\/10.3390\/math11112438","journal-title":"Mathematics"},{"key":"716_CR106","doi-asserted-by":"publisher","unstructured":"Thaher, T., Arman, N.: Efficient multi-swarm binary harris hawks optimization as a feature selection approach for software fault prediction. In: 2020 11th International Conference on Information and Communication Systems (ICICS), pp. 249\u2013254. IEEE (2020). https:\/\/doi.org\/10.1109\/ICICS49469.2020.239557","DOI":"10.1109\/ICICS49469.2020.239557"},{"key":"716_CR107","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-08772-x","author":"NA Alawad","year":"2023","unstructured":"Alawad, N.A., Abed-alguni, B.H., Al-Betar, M.A., Jaradat, A.: Binary improved white shark algorithm for intrusion detection systems. Neural Comput. Appl. (2023). https:\/\/doi.org\/10.1007\/s00521-023-08772-x","journal-title":"Neural Comput. Appl."},{"issue":"15","key":"716_CR108","doi-asserted-by":"publisher","first-page":"11741","DOI":"10.3390\/su151511741","volume":"15","author":"A Fathy","year":"2023","unstructured":"Fathy, A., Alanazi, A.: An efficient white shark optimizer for enhancing the performance of proton exchange membrane fuel cells. Sustainability 15(15), 11741 (2023). https:\/\/doi.org\/10.3390\/su151511741","journal-title":"Sustainability"},{"key":"716_CR109","doi-asserted-by":"publisher","unstructured":"Saadi, A.A., Soukane, A., Meraihi, Y., Gabis, A.B., Ramdane-Cherif, A., Yahia, S.: An enhanced white shark optimization algorithm for unmanned aerial vehicles placement. In: EAI International Conference on Computational Intelligence and Communications, pp. 27\u201342. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-34459-6_3","DOI":"10.1007\/978-3-031-34459-6_3"},{"key":"716_CR110","doi-asserted-by":"publisher","unstructured":"Taleb, S.M., Meraihi, Y., Ramdane-Cherif, A., Gabis, A.B., Acheli, D.: Enhanced white shark optimization algorithm for the mesh routers placement problem with service priority in wireless mesh networks. In: Workshop on Mining Data for Financial Applications, pp. 183\u2013196. Springer (2022).https:\/\/doi.org\/10.1007\/978-981-99-1620-7_15","DOI":"10.1007\/978-981-99-1620-7_15"},{"key":"716_CR111","doi-asserted-by":"publisher","unstructured":"Wu, N., Zhang, G., Fan, S., Huang, Y.: Optimal scheduling of grid connected microgrid based on improved white shark algorithm. In: 2022 China Automation Congress (CAC), pp. 5712\u20135717. IEEE (2022).https:\/\/doi.org\/10.1109\/CAC57257.2022.10055618","DOI":"10.1109\/CAC57257.2022.10055618"},{"issue":"12","key":"716_CR112","doi-asserted-by":"publisher","first-page":"5630","DOI":"10.3390\/s23125630","volume":"23","author":"Y Li","year":"2023","unstructured":"Li, Y., Tang, B., Huang, B., Xue, X.: A dual-optimization fault diagnosis method for rolling bearings based on hierarchical slope entropy and svm synergized with shark optimization algorithm. Sensors 23(12), 5630 (2023). https:\/\/doi.org\/10.3390\/s23125630","journal-title":"Sensors"},{"key":"716_CR113","doi-asserted-by":"publisher","DOI":"10.22266\/ijies2024.0229.14","author":"GA Chandok","year":"2024","unstructured":"Chandok, G.A., Rexy, V., Basha, H.A., Selvi, H.: Enhancing bankruptcy prediction with white shark optimizer and deep learning: A hybrid approach for accurate financial risk assessment. Int. J. Intell. Eng. Syst. (2024). https:\/\/doi.org\/10.22266\/ijies2024.0229.14","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"716_CR114","doi-asserted-by":"publisher","unstructured":"Durga, N., Gayathri, T., Kumari, K.R., Madhavi, T.: Clustering based hybrid optimized model for effective data transmission. In: International Conference on Cognitive Computing and Cyber Physical Systems, pp. 338\u2013351. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-48891-7_30","DOI":"10.1007\/978-3-031-48891-7_30"},{"issue":"1","key":"716_CR115","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-023-00726-3","volume":"10","author":"N Parveen","year":"2023","unstructured":"Parveen, N., Chakrabarti, P., Hung, B.T., Shaik, A.: Twitter sentiment analysis using hybrid gated attention recurrent network. J. Big Data 10(1), 1\u201329 (2023). https:\/\/doi.org\/10.1186\/s40537-023-00726-3","journal-title":"J. Big Data"},{"key":"716_CR116","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120117","volume":"228","author":"Q Xing","year":"2023","unstructured":"Xing, Q., Wang, J., Jiang, H., Wang, K.: Research of a novel combined deterministic and probabilistic forecasting system for air pollutant concentration. Expert Syst. Appl. 228, 120117 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.120117","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"716_CR117","doi-asserted-by":"publisher","first-page":"2363","DOI":"10.1007\/s11277-023-10675-y","volume":"132","author":"K Kumaresan","year":"2023","unstructured":"Kumaresan, K., Rohith Bhat, C., Lalitha Devi, K.: A novel fuzzy marine white shark optimization based efficient routing and enhancing network lifetime in manet. Wirel. Pers. Commun. 132(4), 2363\u20132385 (2023). https:\/\/doi.org\/10.1007\/s11277-023-10675-y","journal-title":"Wirel. Pers. Commun."},{"key":"716_CR118","doi-asserted-by":"publisher","first-page":"100243","DOI":"10.1016\/j.prime.2023.100243","volume":"5","author":"VSDM Sahu","year":"2023","unstructured":"Sahu, V.S.D.M., Samal, P., Panigrahi, C.K.: Tyrannosaurus optimization algorithm: a new nature-inspired meta-heuristic algorithm for solving optimal control problems. e-Prime-Adv. Electr. Eng. Electron. Energy 5, 100243 (2023). https:\/\/doi.org\/10.1016\/j.prime.2023.100243","journal-title":"e-Prime-Adv. Electr. Eng. Electron. Energy"},{"key":"716_CR119","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-231776","author":"R Chandana Mani","year":"2023","unstructured":"Chandana Mani, R., Kamalakannan, J.: Computer-aided diagnosis using white shark optimizer with attention-based deep learning for breast cancer classification. J. Intell. Fuzzy Syst. (2023). https:\/\/doi.org\/10.3233\/JIFS-231776","journal-title":"J. Intell. Fuzzy Syst."},{"key":"716_CR120","doi-asserted-by":"publisher","DOI":"10.32604\/csse.2023.034456","author":"IM Alwayle","year":"2023","unstructured":"Alwayle, I.M., Al-onazi, B.B., Nour, M.K., Alalayah, K.M., Alaidarous, K.M., Ahmed, I.A., Mehanna, A.S., Motwakel, A.: Automated spam review detection using hybrid deep learning on Arabic opinions. Comput. Syst. Sci. Eng. (2023). https:\/\/doi.org\/10.32604\/csse.2023.034456","journal-title":"Comput. Syst. Sci. Eng."},{"key":"716_CR121","doi-asserted-by":"publisher","unstructured":"Gude, M.K., Salma, U.: A novel white shark optimizer for optimal parameter selection of power system oscillation damper. In: Soft Computing Applications in Modern Power and Energy Systems: Select Proceedings of EPREC 2022, pp. 217\u2013226. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-19-8353-5_15","DOI":"10.1007\/978-981-19-8353-5_15"},{"key":"716_CR122","doi-asserted-by":"publisher","DOI":"10.1177\/0958305X231153970","author":"C Kalaivani","year":"2023","unstructured":"Kalaivani, C., Umasankar, L., Badachi, C., Karthikumar, K.: An enhanced approach-based grid flexibility analysis for combined heat and power systems with variable renewable energy systems. Energy Environ. (2023). https:\/\/doi.org\/10.1177\/0958305X231153970","journal-title":"Energy Environ."},{"issue":"10","key":"716_CR123","doi-asserted-by":"publisher","first-page":"3983","DOI":"10.3390\/en16103983","volume":"16","author":"ES Ali","year":"2023","unstructured":"Ali, E.S., Abd Elazim, S.M., Hakmi, S.H., Mosaad, M.I.: Optimal allocation and size of renewable energy sources as distributed generations using shark optimization algorithm in radial distribution systems. Energies 16(10), 3983 (2023)","journal-title":"Energies"},{"key":"716_CR124","doi-asserted-by":"publisher","DOI":"10.3390\/su14106049","author":"MA Ali","year":"2022","unstructured":"Ali, M.A., Kamel, S., Hassan, M.H., Ahmed, E.M., Alanazi, M.: Optimal power flow solution of power systems with renewable energy sources using white sharks algorithm. Sustainability (2022). https:\/\/doi.org\/10.3390\/su14106049","journal-title":"Sustainability"},{"key":"716_CR125","doi-asserted-by":"publisher","DOI":"10.3390\/su151511741","author":"A Fathy","year":"2023","unstructured":"Fathy, A., Alanazi, A.: An efficient white shark optimizer for enhancing the performance of proton exchange membrane fuel cells. Sustainability (2023). https:\/\/doi.org\/10.3390\/su151511741","journal-title":"Sustainability"},{"key":"716_CR126","doi-asserted-by":"publisher","first-page":"121474","DOI":"10.1016\/j.eswa.2023.121474","volume":"237","author":"N Amor","year":"2024","unstructured":"Amor, N., Tayyab Noman, M., Petru, M., Sebastian, N., Balram, D.: Design and optimization of machinability of zno embedded-glass fiber reinforced polymer composites with a modified white shark optimizer. Expert Syst. Appl. 237, 121474 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.121474","journal-title":"Expert Syst. Appl."},{"key":"716_CR127","doi-asserted-by":"publisher","first-page":"120137","DOI":"10.1016\/j.eswa.2023.120137","volume":"226","author":"S Kumar","year":"2023","unstructured":"Kumar, S., Sharma, N.K., Kumar, N.: Wsomark: an adaptive dual-purpose color image watermarking using white shark optimizer and Levenberg\u2013Marquardt bpnn. Expert Syst. Appl. 226, 120137 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.120137","journal-title":"Expert Syst. Appl."},{"key":"716_CR128","doi-asserted-by":"publisher","DOI":"10.32604\/csse.2023.039207","author":"T Althobaiti","year":"2023","unstructured":"Althobaiti, T., Sanjalawe, Y., Ramzan, N.: Securing cloud computing from flash crowd attack using ensemble intrusion detection system. Comput. Syst. Sci. Eng. (2023). https:\/\/doi.org\/10.32604\/csse.2023.039207","journal-title":"Comput. Syst. Sci. Eng."},{"key":"716_CR129","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13553","author":"F Daneshfar","year":"2024","unstructured":"Daneshfar, F., Aghajani, M.J.: Enhanced text classification through an improved discrete laying chicken algorithm. Expert Syst. (2024). https:\/\/doi.org\/10.1111\/exsy.13553","journal-title":"Expert Syst."},{"key":"716_CR130","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.8033","author":"S Jafari","year":"2024","unstructured":"Jafari, S., Aghaee-Maybodi, N.: Detection of phishing addresses and pages with a data set balancing approach by generative adversarial network (gan) and convolutional neural network (cnn) optimized with swarm intelligence. Concurr. Comput. Pract. Exp. (2024). https:\/\/doi.org\/10.1002\/cpe.8033","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"716_CR131","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3221184","author":"S Pal","year":"2022","unstructured":"Pal, S., Sillitti, A.: Cross-project defect prediction: a literature review. IEEE Access (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3221184","journal-title":"IEEE Access"},{"issue":"7","key":"716_CR132","doi-asserted-by":"publisher","first-page":"5723","DOI":"10.1007\/s11831-022-09787-8","volume":"29","author":"M Nevendra","year":"2022","unstructured":"Nevendra, M., Singh, P.: A survey of software defect prediction based on deep learning. Arch. Comput. Methods Eng. 29(7), 5723\u20135748 (2022). https:\/\/doi.org\/10.1007\/s11831-022-09787-8","journal-title":"Arch. Comput. Methods Eng."},{"issue":"8","key":"716_CR133","doi-asserted-by":"publisher","first-page":"4131","DOI":"10.1007\/s41870-023-01528-9","volume":"15","author":"T Siddiqui","year":"2023","unstructured":"Siddiqui, T., Mustaqeem, M.: Performance evaluation of software defect prediction with nasa dataset using machine learning techniques. Int. J. Inf. Technol. 15(8), 4131\u20134139 (2023). https:\/\/doi.org\/10.1007\/s41870-023-01528-9","journal-title":"Int. J. Inf. Technol."},{"key":"716_CR134","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3287326","author":"I Mehmood","year":"2023","unstructured":"Mehmood, I., Shahid, S., Hussain, H., Khan, I., Ahmad, S., Rahman, S., Ullah, N., Huda, S.: A novel approach to improve software defect prediction accuracy using machine learning. IEEE Access (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3287326","journal-title":"IEEE Access"},{"issue":"4","key":"716_CR135","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.jer.2023.10.038","volume":"11","author":"X Dong","year":"2023","unstructured":"Dong, X., Liang, Y., Miyamoto, S., Yamaguchi, S.: Ensemble learning based software defect prediction. J. Eng. Res. 11(4), 377\u2013391 (2023). https:\/\/doi.org\/10.1016\/j.jer.2023.10.038","journal-title":"J. Eng. Res."},{"key":"716_CR136","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-024-02067-7","author":"A Mostefai","year":"2024","unstructured":"Mostefai, A.: Using sum product networks to predict defects in software systems. Int. J. Inf. Technol. (2024). https:\/\/doi.org\/10.1007\/s41870-024-02067-7","journal-title":"Int. J. Inf. Technol."},{"issue":"1","key":"716_CR137","doi-asserted-by":"publisher","first-page":"54","DOI":"10.3390\/info14010054","volume":"14","author":"T Wongvorachan","year":"2023","unstructured":"Wongvorachan, T., He, S., Bulut, O.: A comparison of undersampling, oversampling, and smote methods for dealing with imbalanced classification in educational data mining. Information 14(1), 54 (2023). https:\/\/doi.org\/10.3390\/info14010054","journal-title":"Information"},{"key":"716_CR138","doi-asserted-by":"publisher","unstructured":"Ciubotariu, G., Czibula, G., Czibula, I.G., Chelaru, I.-G.: Uncovering behavioural patterns of one: and binary-class svm-based software defect predictors. In: ICSOFT, pp. 249\u2013257. SCITEPRESS \u2013 Science and Technology Publications (2023). https:\/\/doi.org\/10.5220\/0012052700003538","DOI":"10.5220\/0012052700003538"},{"issue":"5","key":"716_CR139","doi-asserted-by":"publisher","first-page":"5478","DOI":"10.1002\/cpe.5478","volume":"32","author":"X Cai","year":"2020","unstructured":"Cai, X., Niu, Y., Geng, S., Zhang, J., Cui, Z., Li, J., Chen, J.: An under-sampled software defect prediction method based on hybrid multi-objective cuckoo search. Concurr. Comput. Pract. Exp. 32(5), 5478 (2020). https:\/\/doi.org\/10.1002\/cpe.5478","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"716_CR140","doi-asserted-by":"publisher","first-page":"121549","DOI":"10.1016\/j.eswa.2023.121549","volume":"237","author":"Z Sun","year":"2024","unstructured":"Sun, Z., Wang, G., Li, P., Wang, H., Zhang, M., Liang, X.: An improved random forest based on the classification accuracy and correlation measurement of decision trees. Expert Syst. Appl. 237, 121549 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.121549","journal-title":"Expert Syst. Appl."},{"key":"716_CR141","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.measurement.2018.04.069","volume":"125","author":"F Wang","year":"2018","unstructured":"Wang, F., Ma, S., Wang, H., Li, Y., Qin, Z., Zhang, J.: A hybrid model integrating improved flower pollination algorithm-based feature selection and improved random forest for nox emission estimation of coal-fired power plants. Measurement 125, 303\u2013312 (2018). https:\/\/doi.org\/10.1016\/j.measurement.2018.04.069","journal-title":"Measurement"},{"key":"716_CR142","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.procs.2023.01.002","volume":"218","author":"T Sharma","year":"2023","unstructured":"Sharma, T., Jatain, A., Bhaskar, S., Pabreja, K.: Ensemble machine learning paradigms in software defect prediction. Procedia Comput. Sci. 218, 199\u2013209 (2023). https:\/\/doi.org\/10.1016\/j.procs.2023.01.002","journal-title":"Procedia Comput. Sci."},{"key":"716_CR143","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2022.107128","volume":"155","author":"S Stradowski","year":"2023","unstructured":"Stradowski, S., Madeyski, L.: Machine learning in software defect prediction: a business-driven systematic mapping study. Inf. Softw. Technol. 155, 107128 (2023). https:\/\/doi.org\/10.1016\/j.infsof.2022.107128","journal-title":"Inf. Softw. Technol."},{"issue":"6","key":"716_CR144","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.3390\/math11061477","volume":"11","author":"E Rodriguez Sanchez","year":"2023","unstructured":"Rodriguez Sanchez, E., Vazquez Santacruz, E.F., Cervantes Maceda, H.: Effort and cost estimation using decision tree techniques and story points in agile software development. Mathematics 11(6), 1477 (2023). https:\/\/doi.org\/10.3390\/math11061477","journal-title":"Mathematics"},{"issue":"2","key":"716_CR145","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s11334-021-00427-1","volume":"19","author":"S Goyal","year":"2023","unstructured":"Goyal, S.: 3pcge: 3-parent child-based genetic evolution for software defect prediction. Innov. Syst. Softw. Eng. 19(2), 197\u2013216 (2023). https:\/\/doi.org\/10.1007\/s11334-021-00427-1","journal-title":"Innov. Syst. Softw. Eng."},{"issue":"10","key":"716_CR146","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3567550","volume":"55","author":"Y Zhao","year":"2023","unstructured":"Zhao, Y., Damevski, K., Chen, H.: A systematic survey of just-in-time software defect prediction. ACM Comput. Surv. 55(10), 1\u201335 (2023). https:\/\/doi.org\/10.1145\/3567550","journal-title":"ACM Comput. Surv."},{"issue":"7\u20138","key":"716_CR147","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1080\/1206212X.2023.2252117","volume":"45","author":"F Feyzi","year":"2023","unstructured":"Feyzi, F., Daneshdoost, A.: Studying the effectiveness of deep active learning in software defect prediction. Int. J. Comput. Appl. 45(7\u20138), 534\u2013552 (2023). https:\/\/doi.org\/10.1080\/1206212X.2023.2252117","journal-title":"Int. J. Comput. Appl."},{"issue":"12","key":"716_CR148","doi-asserted-by":"publisher","first-page":"2714","DOI":"10.3390\/math11122714","volume":"11","author":"AN Babatunde","year":"2023","unstructured":"Babatunde, A.N., Ogundokun, R.O., Adeoye, L.B., Misra, S.: Software defect prediction using dagging meta-learner-based classifiers. Mathematics 11(12), 2714 (2023). https:\/\/doi.org\/10.3390\/math11122714","journal-title":"Mathematics"},{"key":"716_CR149","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2023.111914","volume":"208","author":"MA Shehab","year":"2024","unstructured":"Shehab, M.A., Khreich, W., Hamou-Lhadj, A., Sedki, I.: Commit-time defect prediction using one-class classification. J. Syst. Softw. 208, 111914 (2024). https:\/\/doi.org\/10.1016\/j.jss.2023.111914","journal-title":"J. Syst. Softw."},{"key":"716_CR150","doi-asserted-by":"publisher","unstructured":"Oleshchenko, L.: Software testing errors classification method using clustering algorithms. In: International Conference On Innovative Computing And Communication, pp. 553\u2013566. Springer (2023). https:\/\/doi.org\/10.1007\/978-981-99-3315-0_42","DOI":"10.1007\/978-981-99-3315-0_42"},{"issue":"4","key":"716_CR151","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1002\/spy2.103","volume":"6","author":"M Banga","year":"2023","unstructured":"Banga, M., Bansal, A.: Proposed software faults detection using hybrid approach. Secur. Priv. 6(4), 103 (2023). https:\/\/doi.org\/10.1002\/spy2.103","journal-title":"Secur. Priv."},{"key":"716_CR152","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3339994","author":"T Tahir","year":"2023","unstructured":"Tahir, T., Gencel, C., Rasool, G., Tariq, U., Rasheed, J., Yeo, S.F., Cevik, T.: Early software defects density prediction: training the international software benchmarking cross projects data using supervised learning. IEEE Access (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3339994","journal-title":"IEEE Access"},{"issue":"4","key":"716_CR153","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1007\/s11219-023-09629-1","volume":"31","author":"R Dinter","year":"2023","unstructured":"Dinter, R., Catal, C., Giray, G., Tekinerdogan, B.: Just-in-time defect prediction for mobile applications: using shallow or deep learning? Softw. Qual. J. 31(4), 1281\u20131302 (2023). https:\/\/doi.org\/10.1007\/s11219-023-09629-1","journal-title":"Softw. Qual. J."},{"issue":"5","key":"716_CR154","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1007\/s42979-023-01979-8","volume":"4","author":"S Dewangan","year":"2023","unstructured":"Dewangan, S., Rao, R.S., Chowdhuri, S.R., Gupta, M.: Severity classification of code smells using machine-learning methods. SN Comput. Sci. 4(5), 564 (2023). https:\/\/doi.org\/10.1007\/s42979-023-01979-8","journal-title":"SN Comput. Sci."},{"key":"716_CR155","doi-asserted-by":"publisher","first-page":"1815","DOI":"10.1016\/j.procs.2023.01.159","volume":"218","author":"S Pandey","year":"2023","unstructured":"Pandey, S., Kumar, K.: Software fault prediction for imbalanced data: a survey on recent developments. Procedia Comput. Sci. 218, 1815\u20131824 (2023). https:\/\/doi.org\/10.1016\/j.procs.2023.01.159","journal-title":"Procedia Comput. Sci."},{"issue":"3","key":"716_CR156","doi-asserted-by":"publisher","first-page":"3392","DOI":"10.1007\/s11227-022-04783-y","volume":"79","author":"M Douiba","year":"2023","unstructured":"Douiba, M., Benkirane, S., Guezzaz, A., Azrour, M.: An improved anomaly detection model for iot security using decision tree and gradient boosting. J. Supercomput. 79(3), 3392\u20133411 (2023). https:\/\/doi.org\/10.1007\/s11227-022-04783-y","journal-title":"J. Supercomput."},{"issue":"12","key":"716_CR157","doi-asserted-by":"publisher","first-page":"8017","DOI":"10.1002\/cpe.8017","volume":"36","author":"L Li","year":"2024","unstructured":"Li, L., Su, R., Zhao, X.: Neighbor cleaning learning based cost-sensitive ensemble learning approach for software defect prediction. Concurr. Comput. Pract. Exp. 36(12), 8017 (2024). https:\/\/doi.org\/10.1002\/cpe.8017","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"716_CR158","doi-asserted-by":"publisher","DOI":"10.1007\/s11219-024-09683-3","author":"H Tao","year":"2024","unstructured":"Tao, H., Niu, X., Xu, L., Fu, L., Cao, Q., Chen, H., Shang, S., Xian, Y.: A comparative study of software defect binomial classification prediction models based on machine learning. Softw. Qual. J. (2024). https:\/\/doi.org\/10.1007\/s11219-024-09683-3","journal-title":"Softw. Qual. J."},{"issue":"6","key":"716_CR159","doi-asserted-by":"publisher","first-page":"332","DOI":"10.3390\/info11060332","volume":"11","author":"EK Ampomah","year":"2020","unstructured":"Ampomah, E.K., Qin, Z., Nyame, G.: Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement. Information 11(6), 332 (2020). https:\/\/doi.org\/10.3390\/info11060332","journal-title":"Information"},{"issue":"7","key":"716_CR160","doi-asserted-by":"publisher","first-page":"3470","DOI":"10.3390\/s23073470","volume":"23","author":"S Mcmurray","year":"2023","unstructured":"Mcmurray, S., Sodhro, A.H.: A study on ml-based software defect detection for security traceability in smart healthcare applications. Sensors 23(7), 3470 (2023). https:\/\/doi.org\/10.3390\/s23073470","journal-title":"Sensors"},{"key":"716_CR161","doi-asserted-by":"publisher","unstructured":"Nascimento, L.P.G., Prudencio, R.B.C., Mota, A.C., Filho, A.d.A.P., Cruz, P.H.A., Oliveira, D.C.C.A.d., Moreira, P.R.S.: Machine learning techniques for escaped defect analysis in software testing. In: Proceedings of the 8th Brazilian Symposium on Systematic and Automated Software Testing, pp. 47\u201353. ACM Digital Library (2023). https:\/\/doi.org\/10.1145\/3624032.3624039","DOI":"10.1145\/3624032.3624039"},{"key":"716_CR162","doi-asserted-by":"publisher","unstructured":"Al-Isawi, M.K., Abdulkader, H.: Software defects detection in explainable machine learning approach. In: International Conference on Emerging Trends and Applications in Artificial Intelligence, pp. 505\u2013519. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-56728-5_42","DOI":"10.1007\/978-3-031-56728-5_42"},{"key":"716_CR163","doi-asserted-by":"publisher","DOI":"10.37190\/e-Inf240103","author":"M Azzeh","year":"2024","unstructured":"Azzeh, M., Nassif, A.B., Talib, M.A., Iqba, H.: Software defect prediction using non-dominated sorting genetic algorithm and k-nearest neighbour classifier. e-Inform. Softw. Eng. J. (2024). https:\/\/doi.org\/10.37190\/e-Inf240103","journal-title":"e-Inform. Softw. Eng. J."},{"key":"716_CR164","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3349496","author":"MK Wolff","year":"2024","unstructured":"Wolff, M.K., Schaathun, H.G., Fougner, A.L., Steinert, M., Volden, R.: Mobile software development kit for real time multivariate blood glucose prediction. IEEE Access (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3349496","journal-title":"IEEE Access"},{"key":"716_CR165","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2024.114342","author":"P Barandier","year":"2024","unstructured":"Barandier, P., Mendes, M., Cardoso, A.J.M.: Comparative analysis of four classification algorithms for fault detection of heat pumps. Energy Build (2024). https:\/\/doi.org\/10.1016\/j.enbuild.2024.114342","journal-title":"Energy Build"},{"key":"716_CR166","doi-asserted-by":"publisher","DOI":"10.1007\/s11334-020-00380-5","author":"Y Khatri","year":"2022","unstructured":"Khatri, Y., Singh, S.K.: Cross project defect prediction: a comprehensive survey with its swot analysis. Innov. Syst. Softw. Eng. (2022). https:\/\/doi.org\/10.1007\/s11334-020-00380-5","journal-title":"Innov. Syst. Softw. Eng."},{"issue":"8","key":"716_CR167","doi-asserted-by":"publisher","first-page":"2749","DOI":"10.3390\/ijerph17082749","volume":"17","author":"V-H Nhu","year":"2020","unstructured":"Nhu, V.-H., Shirzadi, A., Shahabi, H., Singh, S.K., Al-Ansari, N., Clague, J.J., Jaafari, A., Chen, W., Miraki, S., Dou, J., et al.: Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, naive bayes tree, artificial neural network, and support vector machine algorithms. Int. J. Environ. Res. Public Health 17(8), 2749 (2020). https:\/\/doi.org\/10.3390\/ijerph17082749","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"716_CR168","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2020.110592","volume":"167","author":"C Lopez-Martin","year":"2020","unstructured":"Lopez-Martin, C., Villuendas-Rey, Y., Azzeh, M., Nassif, A.B., Banitaan, S.: Transformed k-nearest neighborhood output distance minimization for predicting the defect density of software projects. J. Syst. Softw. 167, 110592 (2020). https:\/\/doi.org\/10.1016\/j.jss.2020.110592","journal-title":"J. Syst. Softw."},{"issue":"2","key":"716_CR169","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1108\/IJICC-11-2023-0385","volume":"17","author":"M Mustaqeem","year":"2024","unstructured":"Mustaqeem, M., Mustajab, S., Alam, M.: A hybrid approach for optimizing software defect prediction using a grey wolf optimization and multilayer perceptron. Int. J. Intell. Comput. Cybern. 17(2), 436\u2013464 (2024). https:\/\/doi.org\/10.1108\/IJICC-11-2023-0385","journal-title":"Int. J. Intell. Comput. Cybern."},{"key":"716_CR170","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-10131-3","author":"S Kassaymeh","year":"2024","unstructured":"Kassaymeh, S., Al-Betar, M.A., Rjoubd, G., Fraihat, S., Abdullah, S., Almasri, A.: Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification. Neural Comput. Appl. (2024). https:\/\/doi.org\/10.1007\/s00521-024-10131-3","journal-title":"Neural Comput. Appl."},{"key":"716_CR171","doi-asserted-by":"publisher","unstructured":"Aleem, S., Capretz, L.F., Ahmed, F.: Benchmarking machine learning technologies for software defect detection (2015). https:\/\/doi.org\/10.48550\/arXiv.1506.07563","DOI":"10.48550\/arXiv.1506.07563"},{"key":"716_CR172","doi-asserted-by":"publisher","DOI":"10.5815\/ijmecs.2020.01.03","author":"A Iqbal","year":"2020","unstructured":"Iqbal, A., Aftab, S.: A classification framework for software defect prediction using multi-filter feature selection technique and mlp. Int. J. Mod. Educ. Comput. Sci. (2020). https:\/\/doi.org\/10.5815\/ijmecs.2020.01.03","journal-title":"Int. J. Mod. Educ. Comput. Sci."},{"key":"716_CR173","doi-asserted-by":"publisher","unstructured":"Anand, K., Jena, A.K., et\u00a0al.: Forecasting software modules with known defects through the quadratic discriminant analysis feature reduction technique. In: 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC), pp. 713\u2013717. IEEE (2024). https:\/\/doi.org\/10.1109\/ESIC60604.2024.10481542","DOI":"10.1109\/ESIC60604.2024.10481542"},{"issue":"4","key":"716_CR174","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1177\/09622802211032705","volume":"31","author":"A Brobbey","year":"2022","unstructured":"Brobbey, A., Wiebe, S., Nettel-Aguirre, A., Josephson, C.B., Williamson, T., Lix, L.M., Sajobi, T.T.: Repeated measures discriminant analysis using multivariate generalized estimation equations. Stat. Methods Med. Res. 31(4), 646\u2013657 (2022). https:\/\/doi.org\/10.1177\/09622802211032705","journal-title":"Stat. Methods Med. Res."},{"issue":"2","key":"716_CR175","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4236\/oalib.1108414","volume":"9","author":"A Abdulhafedh","year":"2022","unstructured":"Abdulhafedh, A.: Comparison between common statistical modeling techniques used in research, including: discriminant analysis vs logistic regression, ridge regression vs lasso, and decision tree vs random forest. Open Access Libr. J. 9(2), 1\u201319 (2022). https:\/\/doi.org\/10.4236\/oalib.1108414","journal-title":"Open Access Libr. J."},{"key":"716_CR176","doi-asserted-by":"publisher","DOI":"10.1016\/j.anucene.2022.109560","volume":"181","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Sun, P.: A fault diagnosis methodology for nuclear power plants based on kernel principle component analysis and quadratic support vector machine. Ann. Nucl. Energy 181, 109560 (2023). https:\/\/doi.org\/10.1016\/j.anucene.2022.109560","journal-title":"Ann. Nucl. Energy"},{"key":"716_CR177","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106996","volume":"126","author":"C Yang","year":"2023","unstructured":"Yang, C., Ma, S., Han, Q.: Robust discriminant latent variable manifold learning for rotating machinery fault diagnosis. Eng. Appl. Artif. Intell. 126, 106996 (2023). https:\/\/doi.org\/10.1016\/j.engappai.2023.106996","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"4","key":"716_CR178","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1007\/s11440-021-01440-1","volume":"17","author":"S Lin","year":"2022","unstructured":"Lin, S., Zheng, H., Han, B., Li, Y., Han, C., Li, W.: Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction. Acta Geotech. 17(4), 1477\u20131502 (2022). https:\/\/doi.org\/10.1007\/s11440-021-01440-1","journal-title":"Acta Geotech."},{"key":"716_CR179","doi-asserted-by":"publisher","unstructured":"Samantaray, R., Das, H.: Performance analysis of machine learning algorithms using bagging ensemble technique for software fault prediction. In: 2023 6th International Conference on Information Systems and Computer Networks (ISCON), pp. 1\u20137. IEEE (2023). https:\/\/doi.org\/10.1109\/ISCON57294.2023.10111952","DOI":"10.1109\/ISCON57294.2023.10111952"},{"issue":"5s","key":"716_CR180","doi-asserted-by":"publisher","first-page":"178187","DOI":"10.17762\/ijritcc.v11i5s.6642","volume":"11","author":"N Gupta","year":"2023","unstructured":"Gupta, N., Sinha, R.R., Goyal, A., Sunda, N., Sharma, D.: Analyze the performance of software by machine learning methods for fault prediction techniques. IJRITCC 11(5s), 178187 (2023). https:\/\/doi.org\/10.17762\/ijritcc.v11i5s.6642","journal-title":"IJRITCC"},{"issue":"3","key":"716_CR181","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1504\/IJCAT.2023.133297","volume":"72","author":"SP Kulkarni","year":"2023","unstructured":"Kulkarni, S.P., Patel, S.: Ensemble-based software fault prediction with two staged data pre-processing. Int. J. Comput. Appl. Technol. 72(3), 212\u2013222 (2023). https:\/\/doi.org\/10.1504\/IJCAT.2023.133297","journal-title":"Int. J. Comput. Appl. Technol."},{"key":"716_CR182","doi-asserted-by":"publisher","unstructured":"Gupta, M., Rajnish, K., Bhattacharya, V.: Effectiveness of ensemble classifier over state-of-art machine learning classifiers for predicting software faults in software modules. In: Machine Learning, Image Processing, Network Security and Data Sciences: Select Proceedings of 3rd International Conference on MIND 2021, pp. 77\u201388. Springer (2023). https:\/\/doi.org\/10.1007\/978-981-19-5868-7_7","DOI":"10.1007\/978-981-19-5868-7_7"},{"key":"716_CR183","doi-asserted-by":"publisher","unstructured":"Olivares-Galindo, J.A., Sanchez-Garcia, A.J., Barrientos-Martinez, R.E., Ocharan-Hernandez, J.O.: Ensemble classifiers in software defect prediction: a systematic literature review. In: 2023 11th International Conference in Software Engineering Research and Innovation (CONISOFT), pp. 1\u20138. IEEE (2023).https:\/\/doi.org\/10.1109\/CONISOFT58849.2023.00011","DOI":"10.1109\/CONISOFT58849.2023.00011"},{"key":"716_CR184","doi-asserted-by":"publisher","unstructured":"Rajbongshi, A., Shakil, R., Akter, B., Lata, M.A., Joarder, M.M.A.: A comprehensive analysis of feature ranking-based fish disease recognition. Array 21, 100329 (2024). https:\/\/doi.org\/10.1016\/j.array.2023.100329","DOI":"10.1016\/j.array.2023.100329"},{"key":"716_CR185","doi-asserted-by":"publisher","unstructured":"Curebal, F., Dag, H.: Enhancing malware classification: A comparative study of feature selection models with parameter optimization. In: 2024 Systems and Information Engineering Design Symposium (SIEDS), pp. 511\u2013516. IEEE (2024). https:\/\/doi.org\/10.1109\/SIEDS61124.2024.10534669","DOI":"10.1109\/SIEDS61124.2024.10534669"},{"issue":"3","key":"716_CR186","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.3390\/app13031639","volume":"13","author":"E Borandag","year":"2023","unstructured":"Borandag, E.: Software fault prediction using an rnn-based deep learning approach and ensemble machine learning techniques. Appl. Sci. 13(3), 1639 (2023). https:\/\/doi.org\/10.3390\/app13031639","journal-title":"Appl. Sci."},{"key":"716_CR187","doi-asserted-by":"publisher","DOI":"10.1016\/j.cola.2023.101253","volume":"78","author":"M Singh","year":"2024","unstructured":"Singh, M., Chhabra, J.K.: Improved software fault prediction using new code metrics and machine learning algorithms. J. Comput. Lang. 78, 101253 (2024). https:\/\/doi.org\/10.1016\/j.cola.2023.101253","journal-title":"J. Comput. Lang."},{"key":"716_CR188","doi-asserted-by":"publisher","unstructured":"Oleshchenko, L.: Machine learning algorithms comparison for software testing errors classification automation. In: International Conference on Computer Science, Engineering and Education Applications, pp. 615\u2013625. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-36118-0_55","DOI":"10.1007\/978-3-031-36118-0_55"},{"key":"716_CR189","doi-asserted-by":"publisher","DOI":"10.1002\/smr.2634","author":"R Mao","year":"2024","unstructured":"Mao, R., Zhang, L., Zhang, X.: Mutation-based data augmentation for software defect prediction. J. Softw. Evol. Process (2024). https:\/\/doi.org\/10.1002\/smr.2634","journal-title":"J. Softw. Evol. Process"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00716-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-024-00716-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00716-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T17:03:30Z","timestamp":1737651810000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-024-00716-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,23]]},"references-count":189,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["716"],"URL":"https:\/\/doi.org\/10.1007\/s44196-024-00716-0","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,23]]},"assertion":[{"value":"8 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"14"}}