{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T21:53:22Z","timestamp":1777499602837,"version":"3.51.4"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T00:00:00Z","timestamp":1685923200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T00:00:00Z","timestamp":1685923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Software Qual J"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11219-023-09642-4","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T08:01:45Z","timestamp":1685952105000},"page":"1241-1280","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Software fault prediction using deep learning techniques"],"prefix":"10.1007","volume":"31","author":[{"given":"Iqra","family":"Batool","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8209-6100","authenticated-orcid":false,"given":"Tamim Ahmed","family":"Khan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,5]]},"reference":[{"key":"9642_CR1","doi-asserted-by":"publisher","first-page":"387","DOI":"10.11591\/ijeecs.v19.i1.pp388-394","volume":"19","author":"M Akour","year":"2020","unstructured":"Akour, M., Alsghaier, H., & Al Qasem, O. (2020). The effectiveness of using deep learning algorithms in predicting students achievements. Indonesian Journal of Electrical Engineering and Computer Science, 19, 387\u2013393.","journal-title":"Indonesian Journal of Electrical Engineering and Computer Science"},{"key":"9642_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJOSSP.2019100101","volume":"10","author":"O Al Qasem","year":"2019","unstructured":"Al Qasem, O., & Akour, M. (2019). Software fault prediction using deep learning algorithms. International Journal of Open Source Software and Processes (IJOSSP), 10, 1\u201319.","journal-title":"International Journal of Open Source Software and Processes (IJOSSP)"},{"key":"9642_CR3","doi-asserted-by":"publisher","first-page":"63945","DOI":"10.1109\/ACCESS.2020.2985290","volume":"8","author":"O Al Qasem","year":"2020","unstructured":"Al Qasem, O., Akour, M., & Alenezi, M. (2020). The influence of deep learning algorithms factors in software fault prediction. IEEE Access, 8, 63945\u201363960.","journal-title":"IEEE Access"},{"key":"9642_CR4","first-page":"e2367","volume":"33","author":"A Ali","year":"2021","unstructured":"Ali, A., & Gravino, C. (2021). An empirical comparison of validation methods for software prediction models. Journal of Software: Evolution and Process, 33, e2367.","journal-title":"Journal of Software: Evolution and Process"},{"key":"9642_CR5","doi-asserted-by":"crossref","unstructured":"Ali, H., & Khan, T. A. (2019). On fault localization using machine learning techniques. In: 2019 International Conference on Frontiers of Information Technology (FIT), IEEE. pp. 357\u20133575.","DOI":"10.1109\/FIT47737.2019.00073"},{"key":"9642_CR6","doi-asserted-by":"publisher","first-page":"85262","DOI":"10.1109\/ACCESS.2019.2924040","volume":"7","author":"SR Aziz","year":"2019","unstructured":"Aziz, S. R., Khan, T., & Nadeem, A. (2019). Experimental validation of inheritance metrics\u2019 impact on software fault prediction. IEEE Access, 7, 85262\u201385275. https:\/\/doi.org\/10.1109\/ACCESS.2019.2924040","journal-title":"IEEE Access"},{"key":"9642_CR7","doi-asserted-by":"publisher","first-page":"170548","DOI":"10.1109\/ACCESS.2020.3022087","volume":"8","author":"SR Aziz","year":"2020","unstructured":"Aziz, S. R., Khan, T. A., & Nadeem, A. (2020). Efficacy of inheritance aspect in software fault prediction - A survey paper. IEEE Access, 8, 170548\u2013170567. https:\/\/doi.org\/10.1109\/ACCESS.2020.3022087","journal-title":"IEEE Access"},{"key":"9642_CR8","doi-asserted-by":"publisher","first-page":"e563","DOI":"10.7717\/peerj-cs.563","volume":"7","author":"SR Aziz","year":"2021","unstructured":"Aziz, S. R., Khan, T. A., & Nadeem, A. (2021). Exclusive use and evaluation of inheritance metrics viability in software fault prediction - an experimental study. PeerJ Computer Science, 7, e563. https:\/\/doi.org\/10.7717\/peerj-cs.563","journal-title":"PeerJ Computer Science"},{"key":"9642_CR9","doi-asserted-by":"publisher","unstructured":"Batool, I., & Khan, T. A. (2022). Software fault prediction using data mining, machine learning and deep learning techniques: A systematic literature review. Computers and Electrical Engineering, 100, 107886. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0045790622001744, https:\/\/doi.org\/10.1016\/j.compeleceng.2022.107886","DOI":"10.1016\/j.compeleceng.2022.107886"},{"key":"9642_CR10","doi-asserted-by":"publisher","first-page":"515","DOI":"10.2298\/CSIS180312039B","volume":"16","author":"E Borandag","year":"2019","unstructured":"Borandag, E., Ozcift, A., Kilinc, D., & Yucalar, F. (2019). Majority vote feature selection algorithm in software fault prediction. Computer Science and Information Systems, 16, 515\u2013539.","journal-title":"Computer Science and Information Systems"},{"key":"9642_CR11","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.infsof.2017.11.005","volume":"96","author":"A Boucher","year":"2018","unstructured":"Boucher, A., & Badri, M. (2018). Software metrics thresholds calculation techniques to predict fault-proneness: An empirical comparison. Information and Software Technology, 96, 38\u201367.","journal-title":"Information and Software Technology"},{"key":"9642_CR12","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/s11219-016-9353-3","volume":"26","author":"D Bowes","year":"2018","unstructured":"Bowes, D., Hall, T., & Petri\u0107, J. (2018). Software defect prediction: do different classifiers find the same defects? Software Quality Journal, 26, 525\u2013552.","journal-title":"Software Quality Journal"},{"key":"9642_CR13","doi-asserted-by":"publisher","first-page":"e5478","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. (2020). An under-sampled software defect prediction method based on hybrid multi-objective cuckoo search. Concurrency and Computation: Practice and Experience, 32, e5478.","journal-title":"Concurrency and Computation: Practice and Experience"},{"key":"9642_CR14","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.1016\/j.eswa.2010.08.022","volume":"38","author":"C Catal","year":"2011","unstructured":"Catal, C., Sevim, U., & Diri, B. (2011). Practical development of an eclipse-based software fault prediction tool using naive bayes algorithm. Expert Systems with Applications, 38, 2347\u20132353.","journal-title":"Expert Systems with Applications"},{"key":"9642_CR15","unstructured":"Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2019). Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models. arXiv preprint arXiv:1908.01529"},{"key":"9642_CR16","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1007\/s00521-016-2437-y","volume":"28","author":"S Chatterjee","year":"2017","unstructured":"Chatterjee, S., Nigam, S., & Roy, A. (2017). Software fault prediction using neuro-fuzzy network and evolutionary learning approach. Neural Computing and Applications, 28, 1221\u20131231.","journal-title":"Neural Computing and Applications"},{"key":"9642_CR17","unstructured":"Dam, H. K., Pham, T., Ng, S. W., Tran, T., Grundy, J., Ghose, A., Kim, T., & Kim, C. J. (2018). A deep tree-based model for software defect prediction. arXiv preprint arXiv:1802.00921"},{"key":"9642_CR18","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1016\/j.asoc.2016.08.025","volume":"49","author":"E Erturk","year":"2016","unstructured":"Erturk, E., & Sezer, E. A. (2016). Iterative software fault prediction with a hybrid approach. Applied Soft Computing, 49, 1020\u20131033.","journal-title":"Applied Soft Computing"},{"key":"9642_CR19","doi-asserted-by":"crossref","unstructured":"Fan, G., Diao, X., Yu, H., Yang, K., & Chen, L. (2019). Software defect prediction via attention-based recurrent neural network. Scientific Programming, 2019.","DOI":"10.1155\/2019\/6230953"},{"key":"9642_CR20","volume-title":"Learning activation functions in deep neural networks","author":"F Farhadi","year":"2017","unstructured":"Farhadi, F. (2017). Learning activation functions in deep neural networks. Montreal (Canada): Ecole Polytechnique."},{"key":"9642_CR21","doi-asserted-by":"crossref","unstructured":"Gao, K., Khoshgoftaar, T. M., Wang, H., & Seliya, N. (2011). Choosing software metrics for defect prediction: An investigation on feature selection techniques. Software: Practice and Experience, 41, 579\u2013606.","DOI":"10.1002\/spe.1043"},{"key":"9642_CR22","unstructured":"Han, J., Pei, J., & Tong, H. (2022). Data mining: Concepts and techniques. Morgan kaufmann."},{"key":"9642_CR23","doi-asserted-by":"crossref","unstructured":"Hoang, T., Dam, H. K., Kamei, Y., Lo, D., & Ubayashi, N. (2019). Deepjit: An end-to-end deep learning framework for just-in-time defect prediction. In: 2019 IEEE\/ACM 16th International Conference on Mining Software Repositories (MSR), IEEE. pp. 34\u201345.","DOI":"10.1109\/MSR.2019.00016"},{"key":"9642_CR24","doi-asserted-by":"publisher","first-page":"2844","DOI":"10.1109\/ACCESS.2017.2785445","volume":"6","author":"S Huda","year":"2017","unstructured":"Huda, S., Alyahya, S., Ali, M. M., Ahmad, S., Abawajy, J., Al-Dossari, H., & Yearwood, J. (2017). A framework for software defect prediction and metric selection. IEEE access, 6, 2844\u20132858.","journal-title":"IEEE access"},{"key":"9642_CR25","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s10586-018-1730-1","volume":"22","author":"R Jayanthi","year":"2019","unstructured":"Jayanthi, R., & Florence, L. (2019). Software defect prediction techniques using metrics based on neural network classifier. Cluster Computing, 22, 77\u201388.","journal-title":"Cluster Computing"},{"key":"9642_CR26","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/j.asoc.2015.07.006","volume":"35","author":"C Jin","year":"2015","unstructured":"Jin, C., & Jin, S. W. (2015). Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization. Applied Soft Computing, 35, 717\u2013725.","journal-title":"Applied Soft Computing"},{"key":"9642_CR27","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1049\/iet-sen.2011.0138","volume":"6","author":"C Jin","year":"2012","unstructured":"Jin, C., Jin, S. W., & Ye, J. M. (2012). Artificial neural network-based metric selection for software fault-prone prediction model. IET software, 6, 479\u2013487.","journal-title":"IET software"},{"key":"9642_CR28","unstructured":"Jones, C., & Bonsignour, O. (2011). The economics of software quality. Addison-Wesley Professional."},{"key":"9642_CR29","doi-asserted-by":"crossref","unstructured":"Jothi, R. (2018). A comparative study of unsupervised learning algorithms for software fault prediction. In: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE. pp. 741\u2013745.","DOI":"10.1109\/ICCONS.2018.8663154"},{"key":"9642_CR30","first-page":"111","volume":"1","author":"B Karlik","year":"2011","unstructured":"Karlik, B., & Olgac, A. V. (2011). Performance analysis of various activation functions in generalized mlp architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1, 111\u2013122.","journal-title":"International Journal of Artificial Intelligence and Expert Systems"},{"key":"9642_CR31","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"9642_CR32","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bengio, Y., Hinton, G., et al. (2015). Deep learning. Nature, 521(7553), 436-444. Google Scholar Cross Ref.","DOI":"10.1038\/nature14539"},{"key":"9642_CR33","doi-asserted-by":"crossref","unstructured":"Li, J., He, P., Zhu, J., & Lyu, M. R. (2017). Software defect prediction via convolutional neural network. In: 2017 IEEE international conference on software quality, reliability and security (QRS), IEEE. pp. 318\u2013328.","DOI":"10.1109\/QRS.2017.42"},{"key":"9642_CR34","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/JAS.2022.105935","volume":"10","author":"X Li","year":"2022","unstructured":"Li, X., Xu, Y., Li, N., Yang, B., & Lei, Y. (2022). Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks. IEEE\/CAA Journal of Automatica Sinica, 10, 121\u2013134.","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"9642_CR35","doi-asserted-by":"publisher","first-page":"83812","DOI":"10.1109\/ACCESS.2019.2925313","volume":"7","author":"H Liang","year":"2019","unstructured":"Liang, H., Yu, Y., Jiang, L., & Xie, Z. (2019). Seml: A semantic lstm model for software defect prediction. IEEE Access, 7, 83812\u201383824.","journal-title":"IEEE Access"},{"key":"9642_CR36","doi-asserted-by":"publisher","first-page":"3289","DOI":"10.1109\/TII.2018.2821768","volume":"14","author":"G Lin","year":"2018","unstructured":"Lin, G., Zhang, J., Luo, W., Pan, L., Xiang, Y., De Vel, O., & Montague, P. (2018). Cross-project transfer representation learning for vulnerable function discovery. IEEE Transactions on Industrial Informatics, 14, 3289\u20133297.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"9642_CR37","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1111\/exsy.12078","volume":"32","author":"R Malhotra","year":"2015","unstructured":"Malhotra, R., & Bansal, A. J. (2015). Fault prediction considering threshold effects of object-oriented metrics. Expert Systems, 32, 203\u2013219.","journal-title":"Expert Systems"},{"key":"9642_CR38","doi-asserted-by":"publisher","first-page":"241","DOI":"10.3745\/JIPS.2012.8.2.241","volume":"8","author":"R Malhotra","year":"2012","unstructured":"Malhotra, R., & Jain, A. (2012). Fault prediction using statistical and machine learning methods for improving software quality. Journal of Information Processing Systems, 8, 241\u2013262.","journal-title":"Journal of Information Processing Systems"},{"key":"9642_CR39","doi-asserted-by":"publisher","first-page":"9847","DOI":"10.1007\/s10586-018-1696-z","volume":"22","author":"C Manjula","year":"2019","unstructured":"Manjula, C., & Florence, L. (2019). Deep neural network based hybrid approach for software defect prediction using software metrics. Cluster Computing, 22, 9847\u20139863.","journal-title":"Cluster Computing"},{"key":"9642_CR40","doi-asserted-by":"crossref","unstructured":"Mercioni, M. A., Tiron, A., & Holban, S. (2019). Dynamic modification of activation function using the backpropagation algorithm in the artificial neural networks. IJACSA International Journal of Advanced Computer Science and Applications, 10.","DOI":"10.14569\/IJACSA.2019.0100406"},{"key":"9642_CR41","doi-asserted-by":"publisher","first-page":"173","DOI":"10.12700\/APH.18.10.2021.10.9","volume":"18","author":"M Nevendra","year":"2021","unstructured":"Nevendra, M., & Singh, P. (2021). Software defect prediction using deep learning. Acta Polytechnica Hungarica, 18, 173\u2013189.","journal-title":"Acta Polytechnica Hungarica"},{"key":"9642_CR42","doi-asserted-by":"crossref","unstructured":"Padhy, N., Satapathy, S., & Singh, R., (2018). State-of-the-art object-oriented metrics and its reusability: A decade review. Smart Computing and Informatics, pp. 431\u2013441.","DOI":"10.1007\/978-981-10-5544-7_42"},{"key":"9642_CR43","doi-asserted-by":"crossref","unstructured":"Peng, S., Jiang, H., Wang, H., Alwageed, H., & Yao, Y. D. (2017). Modulation classification using convolutional neural network based deep learning model. In: 2017 26th Wireless and Optical Communication Conference (WOCC), IEEE. pp. 1\u20135.","DOI":"10.1109\/WOCC.2017.7929000"},{"key":"9642_CR44","doi-asserted-by":"crossref","unstructured":"Phan, A. V., & LeNguyen, M. (2017). Convolutional neural networks on assembly code for predicting software defects. In: 2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES), IEEE. pp. 37\u201342.","DOI":"10.1109\/IESYS.2017.8233558"},{"key":"9642_CR45","doi-asserted-by":"crossref","unstructured":"Pornprasit, C., & Tantithamthavorn, C. (2022). Deeplinedp: Towards a deep learning approach for line-level defect prediction. IEEE Transactions on Software Engineering.","DOI":"10.1109\/TSE.2022.3144348"},{"key":"9642_CR46","doi-asserted-by":"crossref","unstructured":"Radjenovi\u0107, D., Heri\u010dko, M., Torkar, R., & \u017divkovi\u010d, A. (2013). Software fault prediction metrics: A systematic literature review. Information and Software Technology, 55, 1397\u20131418.","DOI":"10.1016\/j.infsof.2013.02.009"},{"key":"9642_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2853073.2853083","volume":"41","author":"SS Rathore","year":"2016","unstructured":"Rathore, S. S., & Kumar, S. (2016). A decision tree regression based approach for the number of software faults prediction. ACM SIGSOFT Software Engineering Notes, 41, 1\u20136.","journal-title":"ACM SIGSOFT Software Engineering Notes"},{"key":"9642_CR48","unstructured":"Rosli, M. M., Teo, N. H. I., Yusop, N. S. M., & Mohamad, N. S. (2011). Fault prediction model for web application using genetic algorithm. In: International conference on computer and software Modeling (IPCSIT), pp. 71\u201377."},{"key":"9642_CR49","first-page":"654","volume":"72","author":"PS Sandhu","year":"2010","unstructured":"Sandhu, P. S., Singh, J., Gupta, V., Kaur, M., Manhas, S., & Sidhu, R. (2010). A k-means based clustering approach for finding faulty modules in open source software systems. World academy of science, Engineering and technology, 72, 654\u2013658.","journal-title":"World academy of science, Engineering and technology"},{"key":"9642_CR50","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85\u2013117.","journal-title":"Neural networks"},{"key":"9642_CR51","first-page":"6","volume":"37","author":"A Shaik","year":"2012","unstructured":"Shaik, A., Reddy, K., & Damodaram, A. (2012). Object oriented software metrics and quality assessment: Current state of the art. International Journal of Computer Applications, 37, 6\u201315.","journal-title":"International Journal of Computer Applications"},{"key":"9642_CR52","first-page":"47","volume":"12","author":"A Sharma","year":"2012","unstructured":"Sharma, A., & Dubey, S. K. (2012). Comparison study and review on object-oriented metrics. Global Journal of Computer Science and Technology, 12, 47\u201356.","journal-title":"Global Journal of Computer Science and Technology"},{"key":"9642_CR53","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). The performance of LSTM and BiLSTM in forecasting time series. In: 2019 IEEE International Conference on Big Data (Big Data), IEEE. pp. 3285\u20133292.","DOI":"10.1109\/BigData47090.2019.9005997"},{"key":"9642_CR54","doi-asserted-by":"publisher","first-page":"13","DOI":"10.5120\/3409-4756","volume":"28","author":"P Singh","year":"2011","unstructured":"Singh, P., Chaudhary, K., & Verma, S. (2011). An investigation of the relationships between software metrics and defects. International Journal of Computer Applications, 28, 13\u201317.","journal-title":"International Journal of Computer Applications"},{"key":"9642_CR55","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1109\/TSMC.2016.2521840","volume":"47","author":"P Singh","year":"2016","unstructured":"Singh, P., Pal, N. R., Verma, S., & Vyas, O. P. (2016). Fuzzy rule-based approach for software fault prediction. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47, 826\u2013837.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"9642_CR56","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139196185","author":"Y Singh","year":"2011","unstructured":"Singh, Y. (2011). Software Testing. Cambridge University Press. https:\/\/doi.org\/10.1017\/CBO9781139196185","journal-title":"Cambridge University Press"},{"key":"9642_CR57","unstructured":"Snuverink, I. (2017). Deep learning for pixelwise classification of hyperspectral images. Ph.D. thesis. Thesis. Delft, Netherlands: Faculty of Mechanical, Maritime and Materials."},{"key":"9642_CR58","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15, 1929\u20131958.","journal-title":"The journal of machine learning research"},{"key":"9642_CR59","doi-asserted-by":"crossref","unstructured":"Suresh, Y., Kumar, L., & Rath, S. K. (2014). Statistical and machine learning methods for software fault prediction using CK metric suite: A comparative analysis. International Scholarly Research Notices, 2014.","DOI":"10.1155\/2014\/251083"},{"key":"9642_CR60","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1016\/j.protcy.2012.10.050","volume":"6","author":"Y Suresh","year":"2012","unstructured":"Suresh, Y., Pati, J., & Rath, S. K. (2012). Effectiveness of software metrics for object-oriented system. Procedia technology, 6, 420\u2013427.","journal-title":"Procedia technology"},{"key":"9642_CR61","first-page":"1","volume-title":"2015 4th International Conference on Reliability","author":"B Suri","year":"2015","unstructured":"Suri, B., & Singhal, S. (2015). Investigating the oo characteristics of software using ckjm metrics. 2015 4th International Conference on Reliability (pp. 1\u20136). IEEE: Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions)."},{"key":"9642_CR62","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.eswa.2018.12.033","volume":"122","author":"H Turabieh","year":"2019","unstructured":"Turabieh, H., Mafarja, M., & Li, X. (2019). Iterated feature selection algorithms with layered recurrent neural network for software fault prediction. Expert systems with applications, 122, 27\u201342.","journal-title":"Expert systems with applications"},{"key":"9642_CR63","doi-asserted-by":"publisher","first-page":"7877","DOI":"10.1007\/s00500-022-06830-5","volume":"26","author":"MN Uddin","year":"2022","unstructured":"Uddin, M. N., Li, B., Ali, Z., Kefalas, P., Khan, I., & Zada, I. (2022). Software defect prediction employing bilstm and bert-based semantic feature. Soft Computing, 26, 7877\u20137891.","journal-title":"Soft Computing"},{"key":"9642_CR64","doi-asserted-by":"crossref","unstructured":"Verma, S., Chug, A., & Singh, A. P. (2020). Impact of hyperparameter tuning on deep learning based estimation of disease severity in grape plant. In: Recent Advances on Soft Computing and Data Mining: Proceedings of the Fourth International Conference on Soft Computing and Data Mining (SCDM 2020), Melaka, Malaysia, January 22\u201323, 2020, Springer. pp. 161\u2013171.","DOI":"10.1007\/978-3-030-36056-6_16"},{"key":"9642_CR65","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1166\/asl.2014.5283","volume":"20","author":"RS Wahono","year":"2014","unstructured":"Wahono, R. S., & Herman, N. S. (2014). Genetic feature selection for software defect prediction. Advanced Science Letters, 20, 239\u2013244.","journal-title":"Advanced Science Letters"},{"key":"9642_CR66","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1109\/TSE.2018.2877678","volume":"46","author":"Z Wan","year":"2018","unstructured":"Wan, Z., Xia, X., Hassan, A. E., Lo, D., Yin, J., & Yang, X. (2018). Perceptions, expectations, and challenges in defect prediction. IEEE Transactions on Software Engineering, 46, 1241\u20131266.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"9642_CR67","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/TR.2020.3047396","volume":"70","author":"H Wang","year":"2021","unstructured":"Wang, H., Zhuang, W., & Zhang, X. (2021). Software defect prediction based on gated hierarchical lstms. IEEE Transactions on Reliability, 70, 711\u2013727.","journal-title":"IEEE Transactions on Reliability"},{"key":"9642_CR68","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1109\/TSE.2018.2877612","volume":"46","author":"S Wang","year":"2018","unstructured":"Wang, S., Liu, T., Nam, J., & Tan, L. (2018). Deep semantic feature learning for software defect prediction. IEEE Transactions on Software Engineering, 46, 1267\u20131293.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"9642_CR69","volume-title":"2012","author":"Y Wu","year":"2012","unstructured":"Wu, Y., Wang, H., Zhang, B., & Du, K. L. (2012). 2012. International Scholarly Research Notices: Using radial basis function networks for function approximation and classification."},{"key":"9642_CR70","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/TR.2020.3040191","volume":"70","author":"J Xu","year":"2020","unstructured":"Xu, J., Wang, F., & Ai, J. (2020). Defect prediction with semantics and context features of codes based on graph representation learning. IEEE Transactions on Reliability, 70, 613\u2013625.","journal-title":"IEEE Transactions on Reliability"},{"key":"9642_CR71","doi-asserted-by":"crossref","unstructured":"Yu, L. (2012). Using negative binomial regression analysis to predict software faults: A study of apache ant.","DOI":"10.5815\/ijitcs.2012.08.08"},{"key":"9642_CR72","doi-asserted-by":"publisher","first-page":"35710","DOI":"10.1109\/ACCESS.2019.2895614","volume":"7","author":"Q Yu","year":"2019","unstructured":"Yu, Q., Qian, J., Jiang, S., Wu, Z., & Zhang, G. (2019a). An empirical study on the effectiveness of feature selection for cross-project defect prediction. IEEE Access, 7, 35710\u201335718.","journal-title":"IEEE Access"},{"key":"9642_CR73","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Si, X., Hu, C., & Zhang, J. (2019b). A review of recurrent neural networks: Lstm cells and network architectures. Neural computation, 31, 1235\u20131270.","journal-title":"Neural computation"},{"key":"9642_CR74","doi-asserted-by":"crossref","unstructured":"Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications Survey Tutor, 1.","DOI":"10.1109\/COMST.2019.2904897"},{"key":"9642_CR75","doi-asserted-by":"publisher","first-page":"108885","DOI":"10.1016\/j.ress.2022.108885","volume":"229","author":"W Zhang","year":"2023","unstructured":"Zhang, W., Wang, Z., & Li, X. (2023). Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis. Reliability Engineering & System Safety, 229, 108885.","journal-title":"Reliability Engineering & System Safety"},{"key":"9642_CR76","doi-asserted-by":"publisher","first-page":"4537","DOI":"10.1016\/j.eswa.2009.12.056","volume":"37","author":"J Zheng","year":"2010","unstructured":"Zheng, J. (2010). Cost-sensitive boosting neural networks for software defect prediction. Expert Systems with Applications, 37, 4537\u20134543.","journal-title":"Expert Systems with Applications"}],"container-title":["Software Quality Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11219-023-09642-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11219-023-09642-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11219-023-09642-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T10:13:49Z","timestamp":1699611229000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11219-023-09642-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,5]]},"references-count":76,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["9642"],"URL":"https:\/\/doi.org\/10.1007\/s11219-023-09642-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2089478\/v1","asserted-by":"object"}]},"ISSN":["0963-9314","1573-1367"],"issn-type":[{"value":"0963-9314","type":"print"},{"value":"1573-1367","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,5]]},"assertion":[{"value":"18 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2023","order":2,"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"}}]}}