{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T11:50:09Z","timestamp":1781869809979,"version":"3.54.5"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:00:00Z","timestamp":1694563200000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/R006660\/2"],"award-info":[{"award-number":["EP\/R006660\/2"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Empir Software Eng"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Just-in-Time Software Defect Prediction (JIT-SDP) operates in an online scenario where additional training data is received over time. Existing online JIT-SDP studies used online Oza ensemble learning methods with Hoeffding Trees as base learners to learn and update JIT-SDP models over time in this scenario. However, it is unknown how these approaches compare against offline learning approaches adapted to operate in online scenarios, and how the use of any other online or offline base learners would affect online JIT-SDP in terms of predictive performance and computational cost. We therefore propose a new approach called Batch Oversampling Rate Boosting (BORB) that is able to use offline base learners in an online JIT-SDP scenario. Based on 10 open source projects, we provide a comprehensive evaluation of BORB with 5 different base learners and the existing online approach Oversampling Rate Boosting with 4 different base learners, both in within-project and cross-project online JIT-SDP scenarios. The results show that offline learning can lead to better predictive performance than the top performing online learning approaches considered in our study, at a higher computational cost. Cross-project data was helpful to improve predictive performance both for offline and online learning, but especially for online learning.<\/jats:p>","DOI":"10.1007\/s10664-023-10335-6","type":"journal-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T07:02:57Z","timestamp":1694588577000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["An investigation of online and offline learning models for online Just-in-Time Software Defect Prediction"],"prefix":"10.1007","volume":"28","author":[{"given":"George G.","family":"Cabral","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2639-0671","authenticated-orcid":false,"given":"Leandro L.","family":"Minku","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adriano L.\u00a0I.","family":"Oliveira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinaldo A.","family":"Pessoa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sadia","family":"Tabassum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"10335_CR1","doi-asserted-by":"crossref","unstructured":"Aggarwal CC et al (2015) Data mining: the textbook, vol 1. Springer","DOI":"10.1007\/978-3-319-14142-8_1"},{"issue":"2","key":"10335_CR2","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(2):281\u2013305","journal-title":"J Mach Learn Res"},{"key":"10335_CR3","first-page":"143","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern Recognition and Machine Learning. Springer, United States, pp 143\u2013144"},{"issue":"1","key":"10335_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"10335_CR5","unstructured":"Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees, 1st edn. CRC Press"},{"issue":"3","key":"10335_CR6","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1109\/TSE.2022.3175789","volume":"49","author":"GG Cabral","year":"2022","unstructured":"Cabral GG, Minku LL (2022) Towards reliable online just-in-time software defect prediction. IEEE Trans on Softw Eng 49(3):1342\u20131358","journal-title":"IEEE Trans on Softw Eng"},{"key":"10335_CR7","doi-asserted-by":"crossref","unstructured":"Cabral GG, Minku LL, Shihab E, Mujahid S (2019) Class imbalance evolution and verification latency in just-in-time software defect prediction. In: 2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE). IEEE, pp 666\u2013676","DOI":"10.1109\/ICSE.2019.00076"},{"issue":"4","key":"10335_CR8","doi-asserted-by":"publisher","first-page":"7346","DOI":"10.1016\/j.eswa.2008.10.027","volume":"36","author":"C Catal","year":"2009","unstructured":"Catal C, Diri B (2009) A systematic review of software fault prediction studies. Exp Syst Appl 36(4):7346\u20137354","journal-title":"Exp Syst Appl"},{"key":"10335_CR9","doi-asserted-by":"crossref","unstructured":"Catolino G, Di\u00a0Nucci D, Ferrucci F (2019) Cross-project just-in-time bug prediction for mobile apps: An empirical assessment. In: 2019 IEEE\/ACM 6th International Conference on Mobile Software Engineering and Systems (MOBILESoft). IEEE, pp 99\u2013110","DOI":"10.1109\/MOBILESoft.2019.00023"},{"key":"10335_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.infsof.2017.08.004","volume":"93","author":"X Chen","year":"2018","unstructured":"Chen X, Zhao Y, Wang Q, Yuan Z (2018) Multi: Multi-objective effort-aware just-in-time software defect prediction. Inf Softw Technol 93:1\u201313","journal-title":"Inf Softw Technol"},{"key":"10335_CR11","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. JMLR 7:1\u201330","journal-title":"JMLR"},{"issue":"4","key":"10335_CR12","first-page":"12","volume":"10","author":"G Ditzler","year":"2015","unstructured":"Ditzler G, Roveri M, Alippi C, Polikar R (2015) Learning in nonstationary environments: A survey. IEEE CIM 10(4):12\u201325","journal-title":"IEEE CIM"},{"key":"10335_CR13","doi-asserted-by":"crossref","unstructured":"Domingos P, Hulten G (2000) Mining high-speed data streams. In: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. pp 71\u201380","DOI":"10.1145\/347090.347107"},{"key":"10335_CR14","doi-asserted-by":"crossref","unstructured":"Eyolfson J, Tan L, Lam P (2011) Do time of day and developer experience affect commit bugginess? In: Proceedings of the 8th Working Conference on Mining Software Repositories. pp 153\u2013162","DOI":"10.1145\/1985441.1985464"},{"key":"10335_CR15","doi-asserted-by":"crossref","unstructured":"Flint S, Chauhan J, Dyer R (2021) Escaping the time pit: Pitfalls and guidelines for using time-based git data. In: MSR. pp 85\u201396","DOI":"10.1109\/MSR52588.2021.00022"},{"issue":"3","key":"10335_CR16","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/s10994-012-5320-9","volume":"90","author":"J Gama","year":"2013","unstructured":"Gama J, Sebastiao R, Rodrigues PP (2013) On evaluating stream learning algorithms. Mach Learn 90(3):317\u2013346","journal-title":"Mach Learn"},{"issue":"14\u201315","key":"10335_CR17","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","volume":"32","author":"MW Gardner","year":"1998","unstructured":"Gardner MW, Dorling S (1998) Artificial neural networks (the multilayer perceptron)\u2014a review of applications in the atmospheric sciences. Atmos Environ 32(14\u201315):2627\u20132636","journal-title":"Atmos Environ"},{"issue":"6","key":"10335_CR18","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1109\/TSE.2011.103","volume":"38","author":"T Hall","year":"2011","unstructured":"Hall T, Beecham S, Bowes D, Gray D, Counsell S (2011) A systematic literature review on fault prediction performance in software engineering. IEEE Trans Softw Eng 38(6):1276\u20131304","journal-title":"IEEE Trans Softw Eng"},{"key":"10335_CR19","doi-asserted-by":"crossref","unstructured":"Huang Q, Xia X, Lo D (2017) Supervised vs unsupervised models: A holistic look at effort-aware just-in-time defect prediction. In: IEEE International Conference on Software Maintenance and Evolution. pp 159\u2013170","DOI":"10.1109\/ICSME.2017.51"},{"issue":"5","key":"10335_CR20","doi-asserted-by":"publisher","first-page":"2823","DOI":"10.1007\/s10664-018-9661-2","volume":"24","author":"Q Huang","year":"2019","unstructured":"Huang Q, Xia X, Lo D (2019) Revisiting supervised and unsupervised models for effort-aware just-in-time defect prediction. Empir Softw Eng 24(5):2823\u20132862","journal-title":"Empir Softw Eng"},{"issue":"5","key":"10335_CR21","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.1007\/s10664-015-9400-x","volume":"21","author":"Y Kamei","year":"2016","unstructured":"Kamei Y, Fukushima T, McIntosh S, Yamashita K, Ubayashi N, Hassan AE (2016) Studying just-in-time defect prediction using cross-project models. Empir Softw Eng 21(5):2072\u20132106","journal-title":"Empir Softw Eng"},{"issue":"6","key":"10335_CR22","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TSE.2012.70","volume":"39","author":"Y Kamei","year":"2012","unstructured":"Kamei Y, Shihab E, Adams B, Hassan AE, Mockus A, Sinha A, Ubayashi N (2012) A large-scale empirical study of just-in-time quality assurance. IEEE Trans Softw Eng 39(6):757\u2013773","journal-title":"IEEE Trans Softw Eng"},{"issue":"2","key":"10335_CR23","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/TSE.2007.70773","volume":"34","author":"S Kim","year":"2008","unstructured":"Kim S, Whitehead EJ, Zhang Y (2008) Classifying software changes: Clean or buggy? IEEE Trans Softw Eng 34(2):181\u2013196","journal-title":"IEEE Trans Softw Eng"},{"key":"10335_CR24","doi-asserted-by":"crossref","unstructured":"Kim S, Zimmermann T, Whitehead\u00a0Jr A E J;\u00a0Zeller (2007) Predicting faults from cached history. In: 29th International Conference on Software Engineering (ICSE\u201907)","DOI":"10.1109\/ICSE.2007.66"},{"key":"10335_CR25","unstructured":"Kleinbaum DG, Dietz K, Gail M, Klein M, Klein M (2002) Logistic regression. Springer"},{"key":"10335_CR26","doi-asserted-by":"publisher","first-page":"106364","DOI":"10.1016\/j.infsof.2020.106364","volume":"126","author":"W Li","year":"2020","unstructured":"Li W, Zhang W, Jia X, Huang Z (2020) Effort-aware semi-supervised just-in-time defect prediction. Inf Softw Technol 126:106364","journal-title":"Inf Softw Technol"},{"issue":"3","key":"10335_CR27","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 XY, Zhu X (2018) Progress on approaches to software defect prediction. Iet Softw 12(3):161\u2013175","journal-title":"Iet Softw"},{"key":"10335_CR28","doi-asserted-by":"crossref","unstructured":"Mantovani RG, Rossi AL, Vanschoren J, Bischl B, De\u00a0Carvalho AC (2015) Effectiveness of random search in svm hyper-parameter tuning. In: 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN.2015.7280664"},{"key":"10335_CR29","doi-asserted-by":"crossref","unstructured":"McCloskey M, Cohen NJ (1989) Catastrophic interference in connectionist networks: The sequential learning problem. In: Psychology of learning and motivation, vol\u00a024. Elsevier, pp 109\u2013165","DOI":"10.1016\/S0079-7421(08)60536-8"},{"issue":"5","key":"10335_CR30","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1109\/TSE.2017.2693980","volume":"44","author":"S McIntosh","year":"2017","unstructured":"McIntosh S, Kamei Y (2017) Are fix-inducing changes a moving target? a longitudinal case study of just-in-time defect prediction. IEEE Trans Softw Eng 44(5):412\u2013428","journal-title":"IEEE Trans Softw Eng"},{"issue":"5","key":"10335_CR31","first-page":"412","volume":"44","author":"S McIntosh","year":"2018","unstructured":"McIntosh S, Kamei Y (2018) Are fix-inducing changes a moving target? a longitudinal case study of just-in-time defect prediction. IEEE TSE 44(5):412\u2013428","journal-title":"IEEE TSE"},{"issue":"5","key":"10335_CR32","first-page":"2658","volume":"22","author":"T Menzies","year":"2017","unstructured":"Menzies T, Yang Y, Mathew G, Boehm B, Hihn J (2017) Negative results for software effort estimation. EMSE 22(5):2658\u20132683","journal-title":"EMSE"},{"issue":"4","key":"10335_CR33","first-page":"537","volume":"39","author":"N Mittas","year":"2012","unstructured":"Mittas N, Angelis L (2012) Ranking and clustering software cost estimation models through a multiple comparisons algorithm. IEEE TSE 39(4):537\u2013551","journal-title":"IEEE TSE"},{"key":"10335_CR34","doi-asserted-by":"crossref","unstructured":"Rosen C, Grawi B, Shihab E (2015) Commitguru: analytics and risk prediction of software commits. In: FSE. ACM, pp 966\u2013969","DOI":"10.1145\/2786805.2803183"},{"issue":"4","key":"10335_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1082983.1083147","volume":"30","author":"J \u015aliwerski","year":"2005","unstructured":"\u015aliwerski J, Zimmermann T, Zeller A (2005) When do changes induce fixes? ACM Sigsoft Softw Eng Notes 30(4):1\u20135","journal-title":"ACM Sigsoft Softw Eng Notes"},{"issue":"12","key":"10335_CR36","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1109\/TSE.2018.2836442","volume":"45","author":"Q Song","year":"2018","unstructured":"Song Q, Guo Y, Shepperd M (2018) A comprehensive investigation of the role of imbalanced learning for software defect prediction. IEEE Trans Softw Eng 45(12):1253\u20131269","journal-title":"IEEE Trans Softw Eng"},{"issue":"1","key":"10335_CR37","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1109\/TSE.2022.3150153","volume":"49","author":"S Tabassum","year":"2022","unstructured":"Tabassum S, Minku LL, Feng D (2022) Cross-project online just-in-time software defect prediction. IEEE Trans Softw Eng 49(1):268\u2013287. https:\/\/doi.org\/10.1109\/TSE.2022.3150153","journal-title":"IEEE Trans Softw Eng"},{"key":"10335_CR38","doi-asserted-by":"crossref","unstructured":"Tabassum S, Minku LL, Feng D, Cabral GG, Song L (2020) An investigation of cross-project learning in online just-in-time software defect prediction. In: 2020 IEEE\/ACM 42nd International Conference on Software Engineering (ICSE). IEEE, pp 554\u2013565","DOI":"10.1145\/3377811.3380403"},{"key":"10335_CR39","doi-asserted-by":"crossref","unstructured":"Tan M, Tan L, Dara S, Mayeux C (2015) Online defect prediction for imbalanced data. In: 2015 IEEE\/ACM 37th IEEE International Conference on Software Engineering, vol\u00a02. IEEE, pp 99\u2013108","DOI":"10.1109\/ICSE.2015.139"},{"key":"10335_CR40","doi-asserted-by":"crossref","unstructured":"Turhan B, Menzies T, Bener AB, Stefano JD (2009) On the relative value of cross-company and within-company data for defect prediction. EMSE 14","DOI":"10.1007\/s10664-008-9103-7"},{"issue":"2","key":"10335_CR41","first-page":"101","volume":"25","author":"A Vargha","year":"2000","unstructured":"Vargha A, Delaney HD (2000) A critique and improvement of the cl common language effect size statistics of mcgraw and wong. J Educ Behav Stat 25(2):101\u2013132","journal-title":"J Educ Behav Stat"},{"key":"10335_CR42","doi-asserted-by":"crossref","unstructured":"Wang S, Minku LL, Yao X (2013) A learning framework for online class imbalance learning. In: 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL). IEEE, pp 36\u201345","DOI":"10.1109\/CIEL.2013.6613138"},{"issue":"5","key":"10335_CR43","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1109\/TKDE.2014.2345380","volume":"27","author":"S Wang","year":"2015","unstructured":"Wang S, Minku LL, Yao X (2015) Resampling-based ensemble methods for online class imbalance learning. IEEE Trans Knowl Data Eng (TKDE) 27(5):1356\u20131368","journal-title":"IEEE Trans Knowl Data Eng (TKDE)"},{"issue":"10","key":"10335_CR44","doi-asserted-by":"publisher","first-page":"4802","DOI":"10.1109\/TNNLS.2017.2771290","volume":"29","author":"S Wang","year":"2018","unstructured":"Wang S, Minku LL, Yao X (2018) A systematic study of online class imbalance learning with concept drift. IEEE Trans Neural Netw Learn Syst 29(10):4802\u20134821","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10335_CR45","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.infsof.2017.03.007","volume":"87","author":"X Yang","year":"2017","unstructured":"Yang X, Lo D, Xia X, Sun J (2017) Tlel: A two-layer ensemble learning approach for just-in-time defect prediction. Inf Softw Technol 87:206\u2013220","journal-title":"Inf Softw Technol"},{"key":"10335_CR46","doi-asserted-by":"crossref","unstructured":"Yang X, Lo D, Xia X, Zhang Y, Sun J (2015) Deep learning for just-in-time defect prediction. In: 2015 IEEE International Conference on Software Quality, Reliability and Security. IEEE, pp 17\u201326","DOI":"10.1109\/QRS.2015.14"},{"key":"10335_CR47","doi-asserted-by":"crossref","unstructured":"Zhu K, Zhang N, Ying S, Zhu D (2020) Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network. IET Software","DOI":"10.1049\/iet-sen.2019.0278"},{"key":"10335_CR48","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.patrec.2020.03.030","volume":"136","author":"Q Zhu","year":"2020","unstructured":"Zhu Q (2020) On the performance of matthews correlation coefficient (MCC) for imbalanced dataset. Pattern Recogn Lett 136:71\u201380","journal-title":"Pattern Recogn Lett"}],"updated-by":[{"DOI":"10.1007\/s10664-023-10422-8","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000}}],"container-title":["Empirical Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-023-10335-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10664-023-10335-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-023-10335-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T14:09:04Z","timestamp":1726668544000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10664-023-10335-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":48,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["10335"],"URL":"https:\/\/doi.org\/10.1007\/s10664-023-10335-6","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s10664-023-10422-8","asserted-by":"object"}]},"ISSN":["1382-3256","1573-7616"],"issn-type":[{"value":"1382-3256","type":"print"},{"value":"1573-7616","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9]]},"assertion":[{"value":"30 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2024","order":3,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":4,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":5,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s10664-023-10422-8","URL":"https:\/\/doi.org\/10.1007\/s10664-023-10422-8","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"121"}}