{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T09:33:04Z","timestamp":1768469584849,"version":"3.49.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,10,8]],"date-time":"2023-10-08T00:00:00Z","timestamp":1696723200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,8]],"date-time":"2023-10-08T00:00:00Z","timestamp":1696723200000},"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\/s11334-023-00542-1","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T07:16:19Z","timestamp":1696835779000},"page":"501-516","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Is deep learning good enough for software defect prediction?"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1882-2435","authenticated-orcid":false,"given":"Sushant Kumar","family":"Pandey","sequence":"first","affiliation":[]},{"given":"Arya","family":"Haldar","sequence":"additional","affiliation":[]},{"given":"Anil Kumar","family":"Tripathi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"issue":"1","key":"542_CR1","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/S0168-9002(02)01908-3","volume":"497","author":"P Arena","year":"2003","unstructured":"Arena P, Basile A, Bucolo M, Fortuna L (2003) Image processing for medical diagnosis using CNN. Nucl Instrum Methods Phys Res Sect A Accel Spectrom Detect Assoc Equip 497(1):174\u2013178","journal-title":"Nucl Instrum Methods Phys Res Sect A Accel Spectrom Detect Assoc Equip"},{"issue":"3","key":"542_CR2","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 Syst Appl 38(3):2347\u20132353","journal-title":"Expert Syst Appl"},{"key":"542_CR3","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"542_CR4","doi-asserted-by":"publisher","DOI":"10.4324\/9781315806730","volume-title":"Ordinal methods for behavioral data analysis","author":"N Cliff","year":"2014","unstructured":"Cliff N (2014) Ordinal methods for behavioral data analysis. Psychology Press, London"},{"issue":"4","key":"542_CR5","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1049\/iet-sen.2019.0149","volume":"14","author":"J Deng","year":"2020","unstructured":"Deng J, Lu L, Qiu S (2020) Software defect prediction via lstm. IET Software 14(4):443\u2013450","journal-title":"IET Software"},{"key":"542_CR6","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6230953","author":"G Fan","year":"2019","unstructured":"Fan G, Diao X, Yu H, Yang K, Chen L (2019) Software defect prediction via attention-based recurrent neural network. Sci Program. https:\/\/doi.org\/10.1155\/2019\/6230953","journal-title":"Sci Program"},{"issue":"5","key":"542_CR7","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1109\/32.815326","volume":"25","author":"NE Fenton","year":"1999","unstructured":"Fenton NE, Neil M (1999) A critique of software defect prediction models. IEEE Trans Softw Eng 25(5):675\u2013689","journal-title":"IEEE Trans Softw Eng"},{"issue":"2","key":"542_CR8","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1214\/aos\/1016218223","volume":"28","author":"J Friedman","year":"2000","unstructured":"Friedman J, Hastie T, Tibshirani R et al (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28(2):337\u2013407","journal-title":"Ann Stat"},{"key":"542_CR9","unstructured":"Garner SR et\u00a0al (1995) Weka: the waikato environment for knowledge analysis. In: Proceedings of the New Zealand computer science research students conference, pp 57\u201364"},{"key":"542_CR10","doi-asserted-by":"crossref","unstructured":"Ghosh D, Singh J (2020) A novel approach of software fault prediction using deep learning technique. In: Automated Software Engineering: A Deep Learning-Based Approach, pp 73\u201391. Springer","DOI":"10.1007\/978-3-030-38006-9_5"},{"key":"542_CR11","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge"},{"issue":"4","key":"542_CR12","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"MA Hearst","year":"1998","unstructured":"Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Appl 13(4):18\u201328","journal-title":"IEEE Intell Syst Appl"},{"key":"542_CR13","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360"},{"issue":"13","key":"542_CR14","doi-asserted-by":"publisher","first-page":"4734","DOI":"10.3390\/s22134734","volume":"22","author":"M Jorayeva","year":"2022","unstructured":"Jorayeva M, Akbulut A, Catal C, Mishra A (2022) Deep learning-based defect prediction for mobile applications. Sensors 22(13):4734","journal-title":"Sensors"},{"key":"542_CR15","doi-asserted-by":"crossref","unstructured":"Katiyar S, Borgohain SK (2021) Comparative evaluation of cnn architectures for image caption generation. arXiv preprint arXiv:2102.11506","DOI":"10.14569\/IJACSA.2020.0111291"},{"key":"542_CR16","unstructured":"Kayalibay B, Jensen G, van\u00a0der Smagt P (2017) Cnn-based segmentation of medical imaging data. arXiv preprint arXiv:1701.03056"},{"key":"542_CR17","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"542_CR18","unstructured":"Koh PW, Nguyen T, Tang YS, Mussmann S, Pierson E, Kim B, Liang P (2020) Concept bottleneck models. In: International Conference on Machine Learning, pp. 5338\u20135348. PMLR"},{"key":"542_CR19","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jss.2017.04.016","volume":"137","author":"L Kumar","year":"2018","unstructured":"Kumar L, Sripada SK, Sureka A, Rath SK (2018) Effective fault prediction model developed using least square support vector machine (lssvm). J Syst Softw 137:686\u2013712","journal-title":"J Syst Softw"},{"key":"542_CR20","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.infsof.2014.07.005","volume":"58","author":"IH Laradji","year":"2015","unstructured":"Laradji IH, Alshayeb M, Ghouti L (2015) Software defect prediction using ensemble learning on selected features. Inf Softw Technol 58:388\u2013402","journal-title":"Inf Softw Technol"},{"key":"542_CR21","doi-asserted-by":"crossref","unstructured":"Li J, He P, Zhu J, Lyu MR (2017) Software defect prediction via convolutional neural network. In: 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS), pp 318\u2013328. IEEE","DOI":"10.1109\/QRS.2017.42"},{"key":"542_CR22","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 (2020) A systematic review of unsupervised learning techniques for software defect prediction. Inf Softw Technol 122:106287","journal-title":"Inf Softw Technol"},{"issue":"3","key":"542_CR23","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M et al (2002) Classification and regression by randomforest. R News 2(3):18\u201322","journal-title":"R News"},{"key":"542_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113156","volume":"147","author":"A Majd","year":"2020","unstructured":"Majd A, Vahidi-Asl M, Khalilian A, Poorsarvi-Tehrani P, Haghighi H (2020) Sldeep: statement-level software defect prediction using deep-learning model on static code features. Expert Syst Appl 147:113156","journal-title":"Expert Syst Appl"},{"key":"542_CR25","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/j.asoc.2014.11.023","volume":"27","author":"R Malhotra","year":"2015","unstructured":"Malhotra R (2015) A systematic review of machine learning techniques for software fault prediction. Appl Soft Comput 27:504\u2013518","journal-title":"Appl Soft Comput"},{"key":"542_CR26","unstructured":"Malohtra R, Yadav HS (2021) An improved cnn-based architecture for within-project software defect prediction. In: Soft Computing and Signal Processing, pp 335\u2013349. Springer"},{"key":"542_CR27","doi-asserted-by":"publisher","first-page":"98754","DOI":"10.1109\/ACCESS.2021.3095559","volume":"9","author":"F Matloob","year":"2021","unstructured":"Matloob F, Ghazal TM, Taleb N, Aftab S, Ahmad M, Khan MA, Abbas S, Soomro TR (2021) Software defect prediction using ensemble learning: A systematic literature review. IEEE Access 9:98754\u201398771","journal-title":"IEEE Access"},{"issue":"3","key":"542_CR28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0247444","volume":"16","author":"HS Munir","year":"2021","unstructured":"Munir HS, Ren S, Mustafa M, Siddique CN, Qayyum S (2021) Attention based gru-lstm for software defect prediction. Plos one 16(3):e0247444","journal-title":"Plos one"},{"key":"542_CR29","volume-title":"Naive bayes classifiers","author":"KP Murphy","year":"2006","unstructured":"Murphy KP et al (2006) Naive bayes classifiers. University of British Columbia, Vancouver"},{"issue":"1","key":"542_CR30","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s10664-012-9218-8","volume":"19","author":"A Okutan","year":"2014","unstructured":"Okutan A, Y\u0131ld\u0131z OT (2014) Software defect prediction using Bayesian networks. Empir Softw Eng 19(1):154\u2013181","journal-title":"Empir Softw Eng"},{"key":"542_CR31","doi-asserted-by":"crossref","unstructured":"Omri S, Sinz C (2020) Deep learning for software defect prediction: a survey. In: Proceedings of the IEEE\/ACM 42nd International Conference on Software Engineering Workshops, pp 209\u2013214","DOI":"10.1145\/3387940.3391463"},{"issue":"10","key":"542_CR32","doi-asserted-by":"publisher","first-page":"2138","DOI":"10.3390\/app9102138","volume":"9","author":"C Pan","year":"2019","unstructured":"Pan C, Lu M, Xu B, Gao H (2019) An improved CNN model for within-project software defect prediction. Appl Sci 9(10):2138","journal-title":"Appl Sci"},{"key":"542_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113085","volume":"144","author":"SK Pandey","year":"2020","unstructured":"Pandey SK, Mishra RB, Tripathi AK (2020) Bpdet: an effective software bug prediction model using deep representation and ensemble learning techniques. Expert Syst Appl 144:113085","journal-title":"Expert Syst Appl"},{"key":"542_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114595","volume":"172","author":"SK Pandey","year":"2021","unstructured":"Pandey SK, Mishra RB, Tripathi AK (2021) Machine learning based methods for software fault prediction: A survey. Expert Syst Appl 172:114595","journal-title":"Expert Syst Appl"},{"issue":"7","key":"542_CR35","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1049\/iet-sen.2020.0119","volume":"14","author":"SK Pandey","year":"2020","unstructured":"Pandey SK, Rathee D, Tripathi AK (2020) Software defect prediction using k-pca and various kernel-based extreme learning machine: an empirical study. IET Softw 14(7):768\u2013782","journal-title":"IET Softw"},{"key":"542_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105924","volume":"197","author":"SK Pandey","year":"2020","unstructured":"Pandey SK, Tripathi AK (2020) Bcv-predictor: a bug count vector predictor of a successive version of the software system. Knowledge-Based Syst 197:105924","journal-title":"Knowledge-Based Syst"},{"key":"542_CR37","doi-asserted-by":"crossref","unstructured":"Pandey SK, Tripathi AK (2021) Class imbalance issue in software defect prediction models by various machine learning techniques: An empirical study. In: 2021 8th International Conference on Smart Computing and Communications (ICSCC), pp 58\u201363. IEEE","DOI":"10.1109\/ICSCC51209.2021.9528170"},{"key":"542_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107541","volume":"233","author":"SK Pandey","year":"2021","unstructured":"Pandey SK, Tripathi AK (2021) Dnnattention: a deep neural network and attention based architecture for cross project defect number prediction. Knowledge-Based Syst 233:107541","journal-title":"Knowledge-Based Syst"},{"issue":"21","key":"542_CR39","doi-asserted-by":"publisher","first-page":"13465","DOI":"10.1007\/s00500-021-06096-3","volume":"25","author":"SK Pandey","year":"2021","unstructured":"Pandey SK, Tripathi AK (2021) An empirical study toward dealing with noise and class imbalance issues in software defect prediction. Soft Comput 25(21):13465\u201313492","journal-title":"Soft Comput"},{"key":"542_CR40","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 (2020) Deep learning based software defect prediction. Neurocomputing 385:100\u2013110","journal-title":"Neurocomputing"},{"key":"542_CR41","unstructured":"Ruck DW, Rogers SK, Kabrisky M, Oxley ME, Suter BW. The multilayer perceptron as an approximation to a bayes optimal discriminant function"},{"issue":"1","key":"542_CR42","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s10664-014-9346-4","volume":"21","author":"D Ryu","year":"2016","unstructured":"Ryu D, Choi O, Baik J (2016) Value-cognitive boosting with a support vector machine for cross-project defect prediction. Empir Softw Eng 21(1):43\u201371","journal-title":"Empir Softw Eng"},{"key":"542_CR43","unstructured":"Sayyad\u00a0Shirabad J, Menzies T (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada. http:\/\/promise.site.uottawa.ca\/SERepository"},{"issue":"9","key":"542_CR44","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.1109\/TSE.2013.11","volume":"39","author":"M Shepperd","year":"2013","unstructured":"Shepperd M, Song Q, Sun Z, Mair C (2013) Data quality: some comments on the NASA software defect datasets. IEEE Trans Softw Eng 39(9):1208\u20131215","journal-title":"IEEE Trans Softw Eng"},{"key":"542_CR45","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958"},{"key":"542_CR46","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-77242-4","volume-title":"Support vector machines","author":"I Steinwart","year":"2008","unstructured":"Steinwart I, Christmann A (2008) Support vector machines. Springer, Berlin"},{"key":"542_CR47","doi-asserted-by":"crossref","unstructured":"Sun Y, Xu L, Li Y, Guo L, Ma Z, Wang Y (2018) Utilizing deep architecture networks of vae in software fault prediction. In: 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA\/IUCC\/BDCloud\/SocialCom\/SustainCom), pp 870\u2013877. IEEE","DOI":"10.1109\/BDCloud.2018.00129"},{"issue":"4","key":"542_CR48","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 HS, Nayak J, Naik B (2021) Bootstrap aggregation ensemble learning-based reliable approach for software defect prediction by using characterized code feature. Innov Syst Softw Eng 17(4):355\u2013379","journal-title":"Innov Syst Softw Eng"},{"key":"542_CR49","unstructured":"Tantithamthavorn CK (2016) Nasa software defect prediction dataset. https:\/\/github.com\/klainfo\/NASADefectDataset"},{"key":"542_CR50","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.infsof.2017.11.008","volume":"96","author":"H Tong","year":"2018","unstructured":"Tong H, Liu B, Wang S (2018) Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning. Inf Softw Technol 96:94\u2013111","journal-title":"Inf Softw Technol"},{"issue":"2","key":"542_CR51","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 Trans Reliab 70(2):711\u2013727","journal-title":"IEEE Trans Reliab"},{"issue":"4","key":"542_CR52","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/s10515-015-0179-1","volume":"23","author":"T Wang","year":"2016","unstructured":"Wang T, Zhang Z, Jing X, Zhang L (2016) Multiple kernel ensemble learning for software defect prediction. Autom Softw Eng 23(4):569\u2013590","journal-title":"Autom Softw Eng"},{"key":"542_CR53","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.infsof.2018.10.004","volume":"106","author":"Z Xu","year":"2019","unstructured":"Xu Z, Liu J, Luo X, Yang Z, Zhang Y, Yuan P, Tang Y, Zhang T (2019) Software defect prediction based on kernel pca and weighted extreme learning machine. Inf Softw Technol 106:182\u2013200","journal-title":"Inf Softw Technol"},{"issue":"8","key":"542_CR54","doi-asserted-by":"publisher","first-page":"3103","DOI":"10.1109\/TSE.2021.3079841","volume":"48","author":"R Yedida","year":"2021","unstructured":"Yedida R, Menzies T (2021) On the value of oversampling for deep learning in software defect prediction. IEEE Trans Softw Eng 48(8):3103\u20133116","journal-title":"IEEE Trans Softw Eng"},{"key":"542_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2021.111026","volume":"180","author":"K Zhu","year":"2021","unstructured":"Zhu K, Ying S, Zhang N, Zhu D (2021) Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network. J Syst Softw 180:111026","journal-title":"J Syst Softw"}],"container-title":["Innovations in Systems and Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-023-00542-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11334-023-00542-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-023-00542-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T07:05:36Z","timestamp":1750316736000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11334-023-00542-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,8]]},"references-count":55,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["542"],"URL":"https:\/\/doi.org\/10.1007\/s11334-023-00542-1","relation":{},"ISSN":["1614-5046","1614-5054"],"issn-type":[{"value":"1614-5046","type":"print"},{"value":"1614-5054","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,8]]},"assertion":[{"value":"10 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2023","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 have no conflicts of interest to declare that are relevant to the content of this article. Sushant Kumar Pandey, Arya Halder, and Anil Kumar Tripathi declare they have no financial interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}