{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T21:46:22Z","timestamp":1757540782571,"version":"3.41.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,5,19]],"date-time":"2024-05-19T00:00:00Z","timestamp":1716076800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,19]],"date-time":"2024-05-19T00:00:00Z","timestamp":1716076800000},"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":["Innovations Syst Softw Eng"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s11334-024-00563-4","type":"journal-article","created":{"date-parts":[[2024,5,19]],"date-time":"2024-05-19T05:01:20Z","timestamp":1716094880000},"page":"727-746","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["IT2F-SEDNN: an interval type-2 fuzzy logic-based stacked ensemble deep learning approach for early phase software dependability analysis"],"prefix":"10.1007","volume":"21","author":[{"given":"Subhashis","family":"Chatterjee","sequence":"first","affiliation":[]},{"given":"Deepjyoti","family":"Saha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,19]]},"reference":[{"key":"563_CR1","doi-asserted-by":"publisher","first-page":"16491","DOI":"10.1007\/s00521-019-04162-4","volume":"32","author":"L Deng","year":"2020","unstructured":"Deng L, Li D, Cai Z, Hong L (2020) Smart IoT information transmission and security optimization model based on chaotic neural computing. Neural Comput Appl 32:16491\u201316504","journal-title":"Neural Comput Appl"},{"key":"563_CR2","doi-asserted-by":"publisher","first-page":"8315","DOI":"10.1007\/s00521-019-04325-3","volume":"32","author":"AA Zaidan","year":"2020","unstructured":"Zaidan AA, Zaidan BB, Alsalem MA et al (2020) Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology. Neural Comput Appl 32:8315\u20138366. https:\/\/doi.org\/10.1007\/s00521-019-04325-3","journal-title":"Neural Comput Appl"},{"key":"563_CR3","doi-asserted-by":"publisher","first-page":"1365","DOI":"10.1109\/TVT.2021.3051966","volume":"70","author":"L Xu","year":"2021","unstructured":"Xu L, Yu X, Gulliver TA (2021) Intelligent outage probability prediction for mobile IoT networks based on an IGWO-Elman neural network. IEEE Trans Veh Technol 70:1365\u20131375. https:\/\/doi.org\/10.1109\/TVT.2021.3051966","journal-title":"IEEE Trans Veh Technol"},{"key":"563_CR4","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1016\/j.conengprac.2006.07.005","volume":"15","author":"TL Johnson","year":"2007","unstructured":"Johnson TL (2007) Improving automation software dependability: a role for formal methods? Control Eng Pract 15:1403\u20131415. https:\/\/doi.org\/10.1016\/j.conengprac.2006.07.005","journal-title":"Control Eng Pract"},{"key":"563_CR5","volume-title":"Software engineering","author":"I Sommerville","year":"2016","unstructured":"Sommerville I (2016) Software engineering, 10th edn. Pearson, London","edition":"10"},{"key":"563_CR6","doi-asserted-by":"crossref","unstructured":"Saha D, Chatterjee S (2022) Optimized decision tree-based early phase software dependability analysis in uncertain environment. In: 2022 International interdisciplinary conference on mathematics, engineering and science (MESIICON). IEEE, pp 1\u20136","DOI":"10.1109\/MESIICON55227.2022.10093237"},{"key":"563_CR7","doi-asserted-by":"publisher","first-page":"45","DOI":"10.4018\/IJSSMET.2020100103","volume":"11","author":"S Chatterjee","year":"2020","unstructured":"Chatterjee S, Maji B (2020) A fuzzy logic-based model for classifying software modules in order to achieve dependable software. Int J Serv Sci Manag Eng Technol 11:45\u201357. https:\/\/doi.org\/10.4018\/IJSSMET.2020100103","journal-title":"Int J Serv Sci Manag Eng Technol"},{"key":"563_CR8","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.asoc.2018.06.032","volume":"70","author":"S Chatterjee","year":"2018","unstructured":"Chatterjee S, Maji B (2018) A Mahalanobis distance based algorithm for assigning rank to the predicted fault prone software modules. Appl Soft Comput J 70:764\u2013772. https:\/\/doi.org\/10.1016\/j.asoc.2018.06.032","journal-title":"Appl Soft Comput J"},{"key":"563_CR9","doi-asserted-by":"crossref","unstructured":"Littlewood B, Strigini L (2000) Software reliability and dependability: a roadmap","DOI":"10.1145\/336512.336551"},{"key":"563_CR10","doi-asserted-by":"crossref","unstructured":"Conejero JM, Figueiredo E, Garcia A, et al (2012) On the relationship of concern metrics and requirements maintainability. In: Information and software technology, pp 212\u2013238","DOI":"10.1016\/j.infsof.2011.09.003"},{"key":"563_CR11","doi-asserted-by":"publisher","first-page":"399","DOI":"10.23940\/ijpe.12.4.p399.mag","volume":"8","author":"DK Yadav","year":"2012","unstructured":"Yadav DK, Chaturvedi SK, Misra RB (2012) Early software defects prediction using fuzzy logic. Int J Perform Eng 8:399\u2013408","journal-title":"Int J Perform Eng"},{"key":"563_CR12","doi-asserted-by":"publisher","first-page":"15241","DOI":"10.1007\/s00521-022-06959-2","volume":"34","author":"HI Kure","year":"2022","unstructured":"Kure HI, Islam S, Mouratidis H (2022) An integrated cyber security risk management framework and risk predication for the critical infrastructure protection. Neural Comput Appl 34:15241\u201315271. https:\/\/doi.org\/10.1007\/s00521-022-06959-2","journal-title":"Neural Comput Appl"},{"key":"563_CR13","doi-asserted-by":"crossref","unstructured":"Al-Jamimi HA, Ahmed M (2012) Prediction of software maintainability using fuzzy logic. In: ICSESS 2012\u2014Proceedings of 2012 IEEE 3rd international conference on software engineering and service science, pp 702\u2013705","DOI":"10.1109\/ICSESS.2012.6269563"},{"key":"563_CR14","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1109\/TSE.1984.5010299","volume":"SE-10","author":"JC Laprie","year":"1984","unstructured":"Laprie JC (1984) Dependability evaluation of software systems in operation. IEEE Trans Softw Eng SE-10:701\u2013714. https:\/\/doi.org\/10.1109\/TSE.1984.5010299","journal-title":"IEEE Trans Softw Eng"},{"key":"563_CR15","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1109\/32.387474","volume":"21","author":"I Lee","year":"1995","unstructured":"Lee I, Iyer RK (1995) Software dependability in the tandem GUARDIAN system. IEEE Trans Softw Eng 21:455\u2013467. https:\/\/doi.org\/10.1109\/32.387474","journal-title":"IEEE Trans Softw Eng"},{"key":"563_CR16","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1109\/32.601075","volume":"23","author":"A Mukherjee","year":"1997","unstructured":"Mukherjee A, Siewiorek DP (1997) Measuring software dependability by robustness benchmarking. IEEE Trans Softw Eng 23:366","journal-title":"IEEE Trans Softw Eng"},{"key":"563_CR17","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1109\/MS.2016.61","volume":"33","author":"G Hatzivasilis","year":"2016","unstructured":"Hatzivasilis G, Papaefstathiou I, Manifavas C (2016) Software security, privacy, and dependability: metrics and measurement. IEEE Softw 33:46\u201354. https:\/\/doi.org\/10.1109\/MS.2016.61","journal-title":"IEEE Softw"},{"key":"563_CR18","doi-asserted-by":"publisher","first-page":"12915","DOI":"10.1007\/s00500-021-06112-6","volume":"26","author":"C Arun","year":"2022","unstructured":"Arun C, Lakshmi C (2022) Genetic algorithm-based oversampling approach to prune the class imbalance issue in software defect prediction. Soft Comput 26:12915\u201312931. https:\/\/doi.org\/10.1007\/s00500-021-06112-6","journal-title":"Soft Comput"},{"key":"563_CR19","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 SN (2022) A sequential ensemble model for software fault prediction. Innov Syst Softw Eng 18:301\u2013308. https:\/\/doi.org\/10.1007\/s11334-021-00390-x","journal-title":"Innov Syst Softw Eng"},{"key":"563_CR20","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1109\/TR.2022.3158949","volume":"71","author":"S Singh Rathore","year":"2022","unstructured":"Singh Rathore S, Singh Chouhan S, Kumar Jain D, Gopal Vachhani A (2022) Generative oversampling methods for handling imbalanced data in software fault prediction; generative oversampling methods for handling imbalanced data in software fault prediction. IEEE Trans Reliab 71:747. https:\/\/doi.org\/10.1109\/TR.2022.3158949","journal-title":"IEEE Trans Reliab"},{"key":"563_CR21","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1109\/TR.2022.3151125","volume":"71","author":"R Kumar","year":"2022","unstructured":"Kumar R, Chaturvedi A, Kailasam L (2022) An unsupervised software fault prediction approach using threshold derivation. IEEE Trans Reliab 71:911\u2013932. https:\/\/doi.org\/10.1109\/TR.2022.3151125","journal-title":"IEEE Trans Reliab"},{"key":"563_CR22","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TR.2022.3149658","volume":"71","author":"S Yang","year":"2022","unstructured":"Yang S, Gou X, Yang M et al (2022) Software bug number prediction based on complex network theory and panel data model. IEEE Trans Reliab 71:162\u2013177. https:\/\/doi.org\/10.1109\/TR.2022.3149658","journal-title":"IEEE Trans Reliab"},{"key":"563_CR23","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/TR.2022.3161581","volume":"71","author":"J Xu","year":"2022","unstructured":"Xu J, Ai J, Liu J, Shi T (2022) ACGDP: an augmented code graph-based system for software defect prediction. IEEE Trans Reliab 71:850\u2013864. https:\/\/doi.org\/10.1109\/TR.2022.3161581","journal-title":"IEEE Trans Reliab"},{"key":"563_CR24","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1109\/TR.2010.2040759","volume":"59","author":"EO Costa","year":"2010","unstructured":"Costa EO, Pozo ATR, Vergilio SR (2010) A genetic programming approach for software reliability modeling. IEEE Trans Reliab 59:222\u2013230. https:\/\/doi.org\/10.1109\/TR.2010.2040759","journal-title":"IEEE Trans Reliab"},{"key":"563_CR25","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1243\/1748006XJRR161","volume":"222","author":"N Fenton","year":"2008","unstructured":"Fenton N, Neil M, Marquez D (2008) Using Bayesian networks to predict software defects and reliability. Proc Inst Mech Eng O J Risk Reliab 222:701\u2013712. https:\/\/doi.org\/10.1243\/1748006XJRR161","journal-title":"Proc Inst Mech Eng O J Risk Reliab"},{"key":"563_CR26","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1109\/TSMC.2016.2521840","volume":"47","author":"P Singh","year":"2017","unstructured":"Singh P, Pal NR, Verma S, Vyas OP (2017) Fuzzy rule-based approach for software fault prediction. IEEE Trans Syst Man Cybern Syst 47:826\u2013837. https:\/\/doi.org\/10.1109\/TSMC.2016.2521840","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"563_CR27","doi-asserted-by":"publisher","first-page":"2214","DOI":"10.1007\/s10489-017-1078-x","volume":"48","author":"S Chatterjee","year":"2018","unstructured":"Chatterjee S, Maji B (2018) A Bayesian belief network based model for predicting software faults in early phase of software development process. Appl Intell 48:2214\u20132228. https:\/\/doi.org\/10.1007\/s10489-017-1078-x","journal-title":"Appl Intell"},{"key":"563_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s21041133","volume":"21","author":"K Filus","year":"2021","unstructured":"Filus K, Boryszko P, Doma\u0144ska J et al (2021) Efficient feature selection for static analysis vulnerability prediction. Sensors 21:1\u201325. https:\/\/doi.org\/10.3390\/s21041133","journal-title":"Sensors"},{"key":"563_CR29","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1007\/s00521-022-08046-y","volume":"35","author":"Y Zhu","year":"2022","unstructured":"Zhu Y, Lin G, Song L, Zhang J (2022) The application of neural network for software vulnerability detection: a review. Neural Comput Appl 35:1279","journal-title":"Neural Comput Appl"},{"key":"563_CR30","doi-asserted-by":"publisher","first-page":"3280","DOI":"10.1109\/TSE.2021.3087402","volume":"48","author":"S Chakraborty","year":"2022","unstructured":"Chakraborty S, Krishna R, Ding Y, Ray B (2022) Deep learning based vulnerability detection: Are we there yet? IEEE Trans Softw Eng 48:3280\u20133296. https:\/\/doi.org\/10.1109\/TSE.2021.3087402","journal-title":"IEEE Trans Softw Eng"},{"key":"563_CR31","doi-asserted-by":"publisher","first-page":"61840","DOI":"10.1109\/ACCESS.2019.2913349","volume":"7","author":"S Jha","year":"2019","unstructured":"Jha S, Kumar R, Hoang Son L et al (2019) Deep learning approach for software maintainability metrics prediction. IEEE Access 7:61840\u201361855. https:\/\/doi.org\/10.1109\/ACCESS.2019.2913349","journal-title":"IEEE Access"},{"key":"563_CR32","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1080\/09720529.2020.1728898","volume":"23","author":"S Gupta","year":"2020","unstructured":"Gupta S, Chug A (2020) Software maintainability prediction using an enhanced random forest algorithm. J Discrete Math Sci Cryptogr 23:441\u2013449. https:\/\/doi.org\/10.1080\/09720529.2020.1728898","journal-title":"J Discrete Math Sci Cryptogr"},{"key":"563_CR33","unstructured":"Mau\u0161a G, Grbac TG, Ba\u0161i\u0107 BD (2012) Multivariate logistic regression prediction of fault-proneness in software modules, pp 698\u2013703"},{"key":"563_CR34","first-page":"43","volume":"11","author":"M Bisi","year":"2015","unstructured":"Bisi M, Goyal NK (2015) Early prediction of software fault-prone module using artificial neural network. Int J Perform Eng 11:43\u201352","journal-title":"Int J Perform Eng"},{"key":"563_CR35","doi-asserted-by":"publisher","unstructured":"Jin C, Jin SW (2015) Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization. Appl Soft Comput 35:717\u2013725. https:\/\/doi.org\/10.1016\/j.asoc.2015.07.006","DOI":"10.1016\/j.asoc.2015.07.006"},{"key":"563_CR36","doi-asserted-by":"publisher","DOI":"10.3390\/app10051745","author":"H Alsawalqah","year":"2020","unstructured":"Alsawalqah H, Hijazi N, Eshtay M et al (2020) Software defect prediction using heterogeneous ensemble classification based on segmented patterns. Appl Sci. https:\/\/doi.org\/10.3390\/app10051745","journal-title":"Appl Sci"},{"key":"563_CR37","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2013.2259203","author":"S Wang","year":"2013","unstructured":"Wang S, Yao X (2013) Using class imbalance learning for software defect prediction. IEEE Trans Reliab. https:\/\/doi.org\/10.1109\/TR.2013.2259203","journal-title":"IEEE Trans Reliab"},{"key":"563_CR38","doi-asserted-by":"publisher","first-page":"4023","DOI":"10.1007\/s00500-015-1738-x","volume":"20","author":"S Chatterjee","year":"2016","unstructured":"Chatterjee S, Maji B (2016) A new fuzzy rule based algorithm for estimating software faults in early phase of development. Soft Comput 20:4023\u20134035. https:\/\/doi.org\/10.1007\/s00500-015-1738-x","journal-title":"Soft Comput"},{"key":"563_CR39","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1002\/spe.2109","volume":"43","author":"D Baca","year":"2013","unstructured":"Baca D, Carlsson B, Petersen K, Lundberg L (2013) Improving software security with static automated code analysis in an industry setting. Softw Pract Exp 43:259\u2013279. https:\/\/doi.org\/10.1002\/spe.2109","journal-title":"Softw Pract Exp"},{"key":"563_CR40","doi-asserted-by":"publisher","first-page":"150672","DOI":"10.1109\/ACCESS.2020.3016774","volume":"8","author":"Z Bilgin","year":"2020","unstructured":"Bilgin Z, Ersoy MA, Soykan EU et al (2020) Vulnerability prediction from source code using machine learning. IEEE Access 8:150672\u2013150684. https:\/\/doi.org\/10.1109\/ACCESS.2020.3016774","journal-title":"IEEE Access"},{"key":"563_CR41","doi-asserted-by":"publisher","unstructured":"Shin Y, Williams L (2008) Is complexity really the enemy of software security? In: Proceedings of the ACM, pp 47\u201350. https:\/\/doi.org\/10.1145\/1456362.1456372","DOI":"10.1145\/1456362.1456372"},{"key":"563_CR42","doi-asserted-by":"publisher","first-page":"2097","DOI":"10.1007\/s13198-014-0325-3","volume":"8","author":"HB Yadav","year":"2017","unstructured":"Yadav HB, Yadav DK (2017) Early software reliability analysis using reliability relevant software metrics. Int J Syst Assur Eng Manag 8:2097\u20132108. https:\/\/doi.org\/10.1007\/s13198-014-0325-3","journal-title":"Int J Syst Assur Eng Manag"},{"key":"563_CR43","doi-asserted-by":"publisher","first-page":"1665","DOI":"10.1109\/TSE.2020.3030745","volume":"48","author":"W Wang","year":"2022","unstructured":"Wang W, Dumont F, Niu N, Horton G (2022) Detecting software security vulnerabilities via requirements dependency analysis. IEEE Trans Softw Eng 48:1665\u20131675. https:\/\/doi.org\/10.1109\/TSE.2020.3030745","journal-title":"IEEE Trans Softw Eng"},{"key":"563_CR44","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1109\/MC.2008.514","volume":"41","author":"G McGraw","year":"2008","unstructured":"McGraw G (2008) Automated code review tools for security. Computer 41:108\u2013111. https:\/\/doi.org\/10.1109\/MC.2008.514","journal-title":"Computer"},{"key":"563_CR45","doi-asserted-by":"crossref","unstructured":"Kaur A, Kaur K, Pathak K (2014) Software maintainability prediction by data mining of software code metrics. In: 2014 International conference on data mining and intelligent computing, ICDMIC 2014. Institute of Electrical and Electronics Engineers Inc","DOI":"10.1109\/ICDMIC.2014.6954262"},{"key":"563_CR46","doi-asserted-by":"publisher","first-page":"2381","DOI":"10.1109\/TFUZZ.2019.2898582","volume":"27","author":"G Ruiz-Garcia","year":"2019","unstructured":"Ruiz-Garcia G, Hagras H, Pomares H, Ruiz IR (2019) Toward a fuzzy logic system based on general forms of interval type-2 fuzzy sets. IEEE Trans Fuzzy Syst 27:2381\u20132395. https:\/\/doi.org\/10.1109\/TFUZZ.2019.2898582","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"563_CR47","doi-asserted-by":"publisher","first-page":"11541","DOI":"10.1007\/s00521-021-05823-z","volume":"33","author":"S Zdravkovi\u0107","year":"2021","unstructured":"Zdravkovi\u0107 S, Vujanovi\u0107 D, Stoki\u0107 M, Pamu\u010dar D (2021) Evaluation of professional driver\u2019s eco-driving skills based on type-2 fuzzy logic model. Neural Comput Appl 33:11541\u201311554. https:\/\/doi.org\/10.1007\/s00521-021-05823-z","journal-title":"Neural Comput Appl"},{"key":"563_CR48","doi-asserted-by":"publisher","first-page":"9649","DOI":"10.1007\/s00521-021-05729-w","volume":"33","author":"A Harrag","year":"2021","unstructured":"Harrag A, Rezk H (2021) Indirect P&O type-2 fuzzy-based adaptive step MPPT for proton exchange membrane fuel cell. Neural Comput Appl 33:9649\u20139662. https:\/\/doi.org\/10.1007\/s00521-021-05729-w","journal-title":"Neural Comput Appl"},{"key":"563_CR49","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.1007\/s00521-019-04212-x","volume":"32","author":"P Yu","year":"2020","unstructured":"Yu P, Yan X (2020) Stock price prediction based on deep neural networks. Neural Comput Appl 32:1609\u20131628. https:\/\/doi.org\/10.1007\/s00521-019-04212-x","journal-title":"Neural Comput Appl"},{"key":"563_CR50","doi-asserted-by":"publisher","first-page":"9579","DOI":"10.1007\/s00521-020-04842-6","volume":"34","author":"PM Shakeel","year":"2022","unstructured":"Shakeel PM, Burhanuddin MA, Desa MI (2022) Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier. Neural Comput Appl 34:9579\u20139592. https:\/\/doi.org\/10.1007\/s00521-020-04842-6","journal-title":"Neural Comput Appl"},{"key":"563_CR51","doi-asserted-by":"publisher","first-page":"14753","DOI":"10.1007\/s00521-020-04830-w","volume":"32","author":"S Mahdavifar","year":"2020","unstructured":"Mahdavifar S, Ghorbani AA (2020) DeNNeS: deep embedded neural network expert system for detecting cyber attacks. Neural Comput Appl 32:14753\u201314780. https:\/\/doi.org\/10.1007\/s00521-020-04830-w","journal-title":"Neural Comput Appl"},{"key":"563_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113239","author":"K Akyol","year":"2020","unstructured":"Akyol K (2020) Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2020.113239","journal-title":"Expert Syst Appl"},{"key":"563_CR53","doi-asserted-by":"publisher","first-page":"15387","DOI":"10.1007\/s00521-020-04986-5","volume":"34","author":"H Rajadurai","year":"2022","unstructured":"Rajadurai H, Gandhi UD (2022) A stacked ensemble learning model for intrusion detection in wireless network. Neural Comput Appl 34:15387\u201315395. https:\/\/doi.org\/10.1007\/s00521-020-04986-5","journal-title":"Neural Comput Appl"},{"key":"563_CR54","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s00521-006-0039-9","volume":"16","author":"Z Kurd","year":"2007","unstructured":"Kurd Z, Kelly T, Austin J (2007) Developing artificial neural networks for safety critical systems. Neural Comput Appl 16:11\u201319. https:\/\/doi.org\/10.1007\/s00521-006-0039-9","journal-title":"Neural Comput Appl"},{"key":"563_CR55","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-018-1730-1","author":"R Jayanthi","year":"2019","unstructured":"Jayanthi R, Florence L (2019) Software defect prediction techniques using metrics based on neural network classifier. Cluster Comput. https:\/\/doi.org\/10.1007\/s10586-018-1730-1","journal-title":"Cluster Comput"},{"key":"563_CR56","doi-asserted-by":"publisher","unstructured":"Shan C, Chen B, Hu C et al (2014) Software defect prediction model based on LLE and SVM. In: IET conference publications, vol 2014. https:\/\/doi.org\/10.1049\/cp.2014.0749","DOI":"10.1049\/cp.2014.0749"},{"key":"563_CR57","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1109\/QSIC.2012.19","volume":"2","author":"J Wang","year":"2012","unstructured":"Wang J, Shen B, Chen Y (2012) Compressed C4.5 models for software defect prediction. Proc Int Conf Qual Softw 2:13\u201316. https:\/\/doi.org\/10.1109\/QSIC.2012.19","journal-title":"Proc Int Conf Qual Softw"},{"key":"563_CR58","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s40595-013-0008-z","volume":"1","author":"G Abaei","year":"2014","unstructured":"Abaei G, Selamat A (2014) A survey on software fault detection based on different prediction approaches. Vietnam J Comput Sci 1:79\u201395. https:\/\/doi.org\/10.1007\/s40595-013-0008-z","journal-title":"Vietnam J Comput Sci"},{"key":"563_CR59","doi-asserted-by":"publisher","DOI":"10.1166\/asl.2014.5640","author":"RS Wahono","year":"2014","unstructured":"Wahono RS, Herman NS, Ahmad S (2014) A comparison framework of classification models for software defect prediction. Adv Sci Lett. https:\/\/doi.org\/10.1166\/asl.2014.5640","journal-title":"Adv Sci Lett"},{"key":"563_CR60","doi-asserted-by":"publisher","unstructured":"Wang T, Li WH (2010) Na\u00efve Bayes software defect prediction model. In: 2010 International conference on computational intelligence and software engineering, CiSE 2010, pp 1\u20134. https:\/\/doi.org\/10.1109\/CISE.2010.5677057","DOI":"10.1109\/CISE.2010.5677057"},{"key":"563_CR61","doi-asserted-by":"publisher","unstructured":"note SI (2019) Neural network based software defect prediction using genetic algorithm and particle swarm optimization. In: 1st International conference on advances in science, engineering and robotics technology 2019, ICASERT 2019, vol 2019, pp 1\u20134. https:\/\/doi.org\/10.1109\/ICASERT.2019.8934642","DOI":"10.1109\/ICASERT.2019.8934642"},{"key":"563_CR62","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1023\/A:1023632027907","volume":"11","author":"TM Khoshgoftaar","year":"2003","unstructured":"Khoshgoftaar TM, Allen EB (2003) Ordering fault-prone software modules. Softw Qual J 11:19\u201337. https:\/\/doi.org\/10.1023\/A:1023632027907","journal-title":"Softw Qual J"}],"container-title":["Innovations in Systems and Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-024-00563-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11334-024-00563-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-024-00563-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T07:05:24Z","timestamp":1750316724000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11334-024-00563-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,19]]},"references-count":62,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["563"],"URL":"https:\/\/doi.org\/10.1007\/s11334-024-00563-4","relation":{},"ISSN":["1614-5046","1614-5054"],"issn-type":[{"type":"print","value":"1614-5046"},{"type":"electronic","value":"1614-5054"}],"subject":[],"published":{"date-parts":[[2024,5,19]]},"assertion":[{"value":"7 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Author(s) have declared that they have no potential conflicts of interest with respect to the research, authorship, and\/or publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research does not involve any animal or human bodies. The data used in this study has been collected from publicly available sources in \u201c\u201d on the internet.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}