{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T13:32:50Z","timestamp":1780407170737,"version":"3.54.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03458-0","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T11:53:42Z","timestamp":1732708422000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Towards Effective Software Defect Prediction Using Machine Learning Techniques"],"prefix":"10.1007","volume":"5","author":[{"given":"Akshat","family":"Pandey","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1183-8509","authenticated-orcid":false,"given":"Akshay","family":"Jadhav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"issue":"9","key":"3458_CR1","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/TSE.2007.70721","volume":"33","author":"T Menzies","year":"2007","unstructured":"Menzies T, Dekhtyar A, Distefano J, Greenwald J. Problems with precision: a response to\" comments on\u2019data mining static code attributes to learn defect predictors\u2019\". IEEE Trans Software Eng. 2007;33(9):637\u201340.","journal-title":"IEEE Trans Software Eng"},{"issue":"5","key":"3458_CR2","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1109\/32.815326","volume":"25","author":"NE Fenton","year":"1999","unstructured":"Fenton NE, Neil M. A critique of software defect prediction models. IEEE Trans Software Eng. 1999;25(5):675\u201389.","journal-title":"IEEE Trans Software Eng"},{"issue":"4","key":"3458_CR3","first-page":"331","volume":"17","author":"MK Thota","year":"2020","unstructured":"Thota MK, Shajin FH, Rajesh P, et al. Survey on software defect prediction techniques. Int J Appl Sci Eng. 2020;17(4):331\u201344.","journal-title":"Int J Appl Sci Eng"},{"key":"3458_CR4","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. Software defect prediction via lstm. IET Softw. 2020;14:443\u201350. https:\/\/doi.org\/10.1049\/iet-sen.2019.0149.","journal-title":"IET Softw"},{"key":"3458_CR5","doi-asserted-by":"publisher","unstructured":"Nam J, Kim, S. Heterogeneous defect prediction. Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (2015) https:\/\/doi.org\/10.1145\/2786805.2786814","DOI":"10.1145\/2786805.2786814"},{"key":"3458_CR6","doi-asserted-by":"publisher","first-page":"759","DOI":"10.17706\/jsw.12.10.759-772","volume":"12","author":"KN Neela","year":"2017","unstructured":"Neela KN, Asif SA, Ami AS, Gias AU. Modeling software defects as anomalies: a case study on promise repository. J Softw. 2017;12:759\u201372. https:\/\/doi.org\/10.17706\/jsw.12.10.759-772.","journal-title":"J Softw"},{"key":"3458_CR7","doi-asserted-by":"publisher","first-page":"3835","DOI":"10.1007\/s40747-022-00848-w","volume":"9","author":"Z Wang","year":"2022","unstructured":"Wang Z, Tong W, Ye G, Chen H, Gong X, Tang Z. Bugpre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks. Complex Amp; Intell Syst. 2022;9:3835\u201355. https:\/\/doi.org\/10.1007\/s40747-022-00848-w.","journal-title":"Complex Amp; Intell Syst"},{"key":"3458_CR8","doi-asserted-by":"publisher","unstructured":"Dam HK, Pham T, Ng SW, Tran T, Grundy J, Ghose A, Kim T, Kim C. Lessons learned from using a deep tree-based model for software defect prediction in practice. 2019 IEEE\/ACM 16th International Conference on Mining Software Repositories (MSR) 2019 https:\/\/doi.org\/10.1109\/msr.2019.00017","DOI":"10.1109\/msr.2019.00017"},{"key":"3458_CR9","doi-asserted-by":"publisher","DOI":"10.1002\/smr.2362","author":"S Guo","year":"2021","unstructured":"Guo S, Dong J, Li H, Wang J. Software defect prediction with imbalanced distribution by radius-synthetic minority over-sampling technique. J Softw Evol Process. 2021. https:\/\/doi.org\/10.1002\/smr.2362.","journal-title":"J Softw Evol Process"},{"issue":"3","key":"3458_CR10","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1049\/iet-sen.2017.0148","volume":"12","author":"Z Li","year":"2018","unstructured":"Li Z, Jing X-Y, Zhu X. Progress on approaches to software defect prediction. IET Softw. 2018;12(3):161\u201375.","journal-title":"IET Softw"},{"issue":"12","key":"3458_CR11","first-page":"4387","volume":"4","author":"H Shukla","year":"2015","unstructured":"Shukla H, Verma DK. A review on software defect prediction. Int J Adv Res Comput Eng Technol (IJARCET). 2015;4(12):4387\u201394.","journal-title":"Int J Adv Res Comput Eng Technol (IJARCET)"},{"key":"3458_CR12","doi-asserted-by":"crossref","unstructured":"Kamei Y, Shihab E. Defect prediction: Accomplishments and future challenges. In: 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), 2016;5;33\u201345. IEEE","DOI":"10.1109\/SANER.2016.56"},{"issue":"5","key":"3458_CR13","first-page":"288","volume":"9","author":"MS Rawat","year":"2012","unstructured":"Rawat MS, Dubey SK. Software defect prediction models for quality improvement: a literature study. Int J Comput Sci Issues (IJCSI). 2012;9(5):288.","journal-title":"Int J Comput Sci Issues (IJCSI)"},{"key":"3458_CR14","unstructured":"Perreault L, Berardinelli S, Izurieta C, Sheppard J. Using classifiers for software defect detection. In: 26th International Conference on Software Engineering and Data Engineering, 2017:2\u20134"},{"key":"3458_CR15","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2018.090212","author":"A Hammouri","year":"2018","unstructured":"Hammouri A, Hammad M, Alnabhan M, Alsarayrah F. Software bug prediction using machine learning approach. Int J Adv Comput Sci Appl. 2018. https:\/\/doi.org\/10.14569\/IJACSA.2018.090212.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"3458_CR16","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. Software defect prediction using ensemble learning: a systematic literature review. IEEe Access. 2021;9:98754\u201371.","journal-title":"IEEe Access"},{"issue":"1","key":"3458_CR17","first-page":"62","volume":"4","author":"SM Abubakar","year":"2020","unstructured":"Abubakar SM, Sufyanu Z, Abubakar MM. A survey of feature selection methods for software defect prediction models. FUDMA J Sci. 2020;4(1):62\u20138.","journal-title":"FUDMA J Sci"},{"issue":"5","key":"3458_CR18","doi-asserted-by":"publisher","first-page":"85","DOI":"10.4236\/jsea.2019.125007","volume":"12","author":"A Alsaeedi","year":"2019","unstructured":"Alsaeedi A, Khan MZ. Software defect prediction using supervised machine learning and ensemble techniques: a comparative study. J Softw Eng Appl. 2019;12(5):85\u2013100.","journal-title":"J Softw Eng Appl"},{"issue":"6","key":"3458_CR19","doi-asserted-by":"publisher","first-page":"5517","DOI":"10.3390\/su15065517","volume":"15","author":"A Khalid","year":"2023","unstructured":"Khalid A, Badshah G, Ayub N, Shiraz M, Ghouse M. Software defect prediction analysis using machine learning techniques. Sustainability. 2023;15(6):5517.","journal-title":"Sustainability"},{"issue":"1","key":"3458_CR20","first-page":"2117339","volume":"2022","author":"MA Khan","year":"2022","unstructured":"Khan MA, Elmitwally NS, Abbas S, Aftab S, Ahmad M, Fayaz M, Khan F. Software defect prediction using artificial neural networks: a systematic literature review. Sci Program. 2022;2022(1):2117339.","journal-title":"Sci Program"},{"issue":"3","key":"3458_CR21","first-page":"35","volume":"2","author":"MS Saeed","year":"2023","unstructured":"Saeed MS, Saleem M. Cross project software defect prediction using machine learning: a review. Int J Comput Innov Sci. 2023;2(3):35\u201352.","journal-title":"Int J Comput Innov Sci"},{"key":"3458_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.111537","volume":"195","author":"G Giray","year":"2023","unstructured":"Giray G, Bennin KE, K\u00f6ksal \u00d6, Babur \u00d6, Tekinerdogan B. On the use of deep learning in software defect prediction. J Syst Softw. 2023;195: 111537.","journal-title":"J Syst Softw"},{"key":"3458_CR23","doi-asserted-by":"crossref","unstructured":"Alnaish ZAH, Hasoon SO. A comparison of classification algorithms for software defect prediction. In: 2023 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), 2023:176\u2013180. IEEE","DOI":"10.1109\/COMNETSAT59769.2023.10420771"},{"issue":"1","key":"3458_CR24","doi-asserted-by":"publisher","first-page":"18","DOI":"10.5815\/ijmecs.2020.01.03","volume":"12","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 Modern Educ Comput Sci. 2020;12(1):18\u201325.","journal-title":"Int J Modern Educ Comput Sci"},{"key":"3458_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107622","volume":"129","author":"W Wu","year":"2024","unstructured":"Wu W, Wang S, Liu B, Shao Y, Xie W. A novel software defect prediction approach via weighted classification based on association rule mining. Eng Appl Artif Intell. 2024;129: 107622.","journal-title":"Eng Appl Artif Intell"},{"issue":"3","key":"3458_CR26","doi-asserted-by":"publisher","first-page":"3615","DOI":"10.1007\/s10586-023-04170-z","volume":"27","author":"NAA Khleel","year":"2024","unstructured":"Khleel NAA, Neh\u00e9z K. Software defect prediction using a bidirectional lstm network combined with oversampling techniques. Clust Comput. 2024;27(3):3615\u201338.","journal-title":"Clust Comput"},{"issue":"3","key":"3458_CR27","doi-asserted-by":"publisher","first-page":"3615","DOI":"10.1007\/s10586-023-04170-z","volume":"27","author":"NAA Khleel","year":"2024","unstructured":"Khleel NAA, Neh\u00e9z K. Software defect prediction using a bidirectional lstm network combined with oversampling techniques. Clust Comput. 2024;27(3):3615\u201338.","journal-title":"Clust Comput"},{"issue":"7","key":"3458_CR28","doi-asserted-by":"publisher","first-page":"2551","DOI":"10.3390\/s22072551","volume":"22","author":"M Jorayeva","year":"2022","unstructured":"Jorayeva M, Akbulut A, Catal C, Mishra A. Machine learning-based software defect prediction for mobile applications: A systematic literature review. Sensors. 2022;22(7):2551.","journal-title":"Sensors"},{"key":"3458_CR29","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. 2022;188: 111245.","journal-title":"J Syst Softw"},{"issue":"16","key":"3458_CR30","doi-asserted-by":"publisher","first-page":"7877","DOI":"10.1007\/s00500-022-06830-5","volume":"26","author":"MN Uddin","year":"2022","unstructured":"Uddin MN, Li B, Ali Z, Kefalas P, Khan I, Zada I. Software defect prediction employing bilstm and bert-based semantic feature. Soft Comput. 2022;26(16):7877\u201391.","journal-title":"Soft Comput"},{"key":"3458_CR31","unstructured":"Sayyad Shirabad J, Menzies TJ. The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada 2005. http:\/\/promise.site.uottawa.ca\/SERepository"},{"key":"3458_CR32","doi-asserted-by":"publisher","unstructured":"Tong H, Liu B, Wang S. Benchmark data sets. Mendeley Data 2017. https:\/\/doi.org\/10.17632\/923xvkk5mm.1","DOI":"10.17632\/923xvkk5mm.1"},{"issue":"1","key":"3458_CR33","doi-asserted-by":"publisher","first-page":"381","DOI":"10.21275\/ART20203995","volume":"9","author":"B Mahesh","year":"2020","unstructured":"Mahesh B. Machine learning algorithms-a review. Int J Sci Res (IJSR). 2020;9(1):381\u20136.","journal-title":"Int J Sci Res (IJSR)."},{"issue":"4","key":"3458_CR34","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.jer.2023.100150","volume":"11","author":"A Jadhav","year":"2023","unstructured":"Jadhav A, Shandilya SK. Reliable machine learning models for estimating effective software development efforts: a comparative analysis. J Eng Res. 2023;11(4):362\u201376.","journal-title":"J Eng Res"},{"issue":"9","key":"3458_CR35","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.3390\/app10093211","volume":"10","author":"H Jeon","year":"2020","unstructured":"Jeon H, Oh S. Hybrid-recursive feature elimination for efficient feature selection. Appl Sci. 2020;10(9):3211.","journal-title":"Appl Sci"},{"issue":"5","key":"3458_CR36","first-page":"2588","volume":"36","author":"A Jadhav","year":"2024","unstructured":"Jadhav A, Kumar Shandilya S. Towards effective feature selection in estimating software effort using machine learning. J Softw: Evol Process. 2024;36(5):2588.","journal-title":"J Softw: Evol Process"},{"issue":"6","key":"3458_CR37","first-page":"61","volume":"13","author":"IK Nti","year":"2021","unstructured":"Nti IK, Nyarko-Boateng O, Aning J, et al. Performance of machine learning algorithms with different k values in k-fold crossvalidation. Int J Inform Technol Comput Sci. 2021;13(6):61\u201371.","journal-title":"Int J Inform Technol Comput Sci"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03458-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03458-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03458-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T12:02:13Z","timestamp":1732708933000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03458-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"references-count":37,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["3458"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03458-0","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]},"assertion":[{"value":"28 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 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":"Both authors have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"1096"}}