{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T10:13:06Z","timestamp":1766139186046,"version":"3.44.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"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":["Autom Softw Eng"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s10515-025-00519-3","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T04:28:25Z","timestamp":1747628905000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Measuring the impact of predictive models on the software project: A cost, service time, and risk evaluation of a metric-based defect severity prediction model"],"prefix":"10.1007","volume":"32","author":[{"given":"Umamaheswara Sharma","family":"B","sequence":"first","affiliation":[]},{"given":"Ravichandra","family":"Sadam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,19]]},"reference":[{"key":"519_CR1","doi-asserted-by":"crossref","unstructured":"Bhutamapuram, U.S.: Some investigations of machine learning models for software defects. In: 2023 IEEE\/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), pp. 259\u2013263 (2023). IEEE","DOI":"10.1109\/ICSE-Companion58688.2023.00070"},{"issue":"10","key":"519_CR2","doi-asserted-by":"publisher","first-page":"8675","DOI":"10.1016\/j.jksuci.2021.09.010","volume":"34","author":"US Bhutamapuram","year":"2022","unstructured":"Bhutamapuram, U.S., Sadam, R.: With-in-project defect prediction using bootstrap aggregation based diverse ensemble learning technique. J. King Saud University-Comput. Inf. Sci. 34(10), 8675\u20138691 (2022)","journal-title":"J. King Saud University-Comput. Inf. Sci."},{"key":"519_CR3","doi-asserted-by":"crossref","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Routledge, ??? (2017)","DOI":"10.1201\/9781315139470"},{"issue":"08","key":"519_CR4","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1142\/S0218001411009093","volume":"25","author":"JS Cardoso","year":"2011","unstructured":"Cardoso, J.S., Sousa, R.: Measuring the performance of ordinal classification. Int. J. Pattern Recognit. Artif. Intell. 25(08), 1173\u20131195 (2011)","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"519_CR5","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"issue":"3","key":"519_CR6","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1037\/0033-2909.114.3.494","volume":"114","author":"N Cliff","year":"1993","unstructured":"Cliff, N.: Dominance statistics: Ordinal analyses to answer ordinal questions. Psychol. Bullet. 114(3), 494 (1993)","journal-title":"Psychol. Bullet."},{"issue":"4\u20135","key":"519_CR7","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1007\/s10664-011-9173-9","volume":"17","author":"M D\u2019Ambros","year":"2012","unstructured":"D\u2019Ambros, M., Lanza, M., Robbes, R.: Evaluating defect prediction approaches: a benchmark and an extensive comparison. Emp. Softw. Eng. 17(4\u20135), 531\u2013577 (2012)","journal-title":"Emp. Softw. Eng."},{"key":"519_CR8","doi-asserted-by":"crossref","unstructured":"Gomes, L.A.F., Silva\u00a0Torres, R., C\u00f4rtes, M.L.: Bug report severity level prediction in open source software: A survey and research opportunities. Inf. Softw. Technol. 115, 58\u201378 (2019)","DOI":"10.1016\/j.infsof.2019.07.009"},{"key":"519_CR9","doi-asserted-by":"crossref","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322\u20131328 (2008). IEEE","DOI":"10.1109\/IJCNN.2008.4633969"},{"issue":"4","key":"519_CR10","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1007\/s11219-019-09490-1","volume":"28","author":"PK Kudjo","year":"2020","unstructured":"Kudjo, P.K., Chen, J., Mensah, S., Amankwah, R., Kudjo, C.: The effect of bellwether analysis on software vulnerability severity prediction models. Softw. Quality J. 28(4), 1413\u20131446 (2020)","journal-title":"Softw. Quality J."},{"key":"519_CR11","doi-asserted-by":"crossref","unstructured":"Kumar, L., Dastidar, T.G., Murthy\u00a0Neti, L.B., Satapathy, S.M., Misra, S., Kocher, V., Padmanabhuni, S.: Deep-learning approach with deepxplore for software defect severity level prediction. In: International Conference on Computational Science and Its Applications, pp. 398\u2013410 (2021). Springer","DOI":"10.1007\/978-3-030-87007-2_28"},{"key":"519_CR12","doi-asserted-by":"crossref","unstructured":"Kumar, L., Gupta, P., Murthy, L.B., Rath, S.K., Satapathy, S.M., Kocher, V., Padmanabhuni, S.: Predicting software defect severity level using sentence embedding and ensemble learning. In: 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 379\u2013386 (2021). IEEE","DOI":"10.1109\/SEAA53835.2021.00056"},{"key":"519_CR13","doi-asserted-by":"crossref","unstructured":"Kumar, L., Kumar, M., Murthy, L.B., Misra, S., Kocher, V., Padmanabhuni, S.: An empirical study on application of word embedding techniques for prediction of software defect severity level. In: 2021 16th Conference on Computer Science and Intelligence Systems (FedCSIS), pp. 477\u2013484 (2021). IEEE","DOI":"10.15439\/2021F100"},{"key":"519_CR14","doi-asserted-by":"crossref","unstructured":"Lamkanfi, A., Demeyer, S., Giger, E., Goethals, B.: Predicting the severity of a reported bug. In: 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), pp. 1\u201310 (2010). IEEE","DOI":"10.1109\/MSR.2010.5463284"},{"issue":"4","key":"519_CR15","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MS.2021.3072577","volume":"38","author":"C Le Goues","year":"2021","unstructured":"Le Goues, C., Pradel, M., Roychoudhury, A., Chandra, S.: Automatic program repair. IEEE Softw. 38(4), 22\u201327 (2021)","journal-title":"IEEE Softw."},{"issue":"4","key":"519_CR16","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/TSE.2008.35","volume":"34","author":"S Lessmann","year":"2008","unstructured":"Lessmann, S., Baesens, B., Mues, C., Pietsch, S.: Benchmarking classification models for software defect prediction: A proposed framework and novel findings. IEEE Trans. Softw. Eng. 34(4), 485\u2013496 (2008)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"519_CR17","unstructured":"Lyu, M.R., et al.: Handbook of Software Reliability Engineering vol. 222. IEEE computer society press CA, ??? (1996)"},{"key":"519_CR18","doi-asserted-by":"crossref","unstructured":"Malhotra, R., Kapoor, N., Jain, R., Biyani, S.: Severity assessment of software defect reports using text classification. Int. J. Comput. Appl. 83(11) (2013)","DOI":"10.5120\/14492-2622"},{"key":"519_CR19","doi-asserted-by":"crossref","unstructured":"Menzies, T., Marcus, A.: Automated severity assessment of software defect reports. In: 2008 IEEE International Conference on Software Maintenance, pp. 346\u2013355 (2008). IEEE","DOI":"10.1109\/ICSM.2008.4658083"},{"key":"519_CR20","unstructured":"Oymak, S., Gulcu, T.C.: Statistical and algorithmic insights for semi-supervised learning with self-training. arXiv:2006.11006 (2020)"},{"key":"519_CR21","unstructured":"Pressman, R.S.: Software Engineering: a Practitioner\u2019s Approach. Palgrave macmillan, ??? (2005)"},{"key":"519_CR22","doi-asserted-by":"publisher","first-page":"46846","DOI":"10.1109\/ACCESS.2019.2909746","volume":"7","author":"WY Ramay","year":"2019","unstructured":"Ramay, W.Y., Umer, Q., Yin, X.C., Zhu, C., Illahi, I.: Deep neural network-based severity prediction of bug reports. IEEE Access 7, 46846\u201346857 (2019)","journal-title":"IEEE Access"},{"key":"519_CR23","doi-asserted-by":"crossref","unstructured":"Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge university press, ??? (2014)","DOI":"10.1017\/CBO9781107298019"},{"key":"519_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.111522","volume":"195","author":"U Sharma","year":"2023","unstructured":"Sharma, U., Sadam, R.: How far does the predictive decision impact the software project? the cost, service time, and failure analysis from a cross-project defect prediction model. J. Syst. Softw. 195, 111522 (2023)","journal-title":"J. Syst. Softw."},{"issue":"3","key":"519_CR25","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1109\/TSE.2010.90","volume":"37","author":"Q Song","year":"2011","unstructured":"Song, Q., Jia, Z., Shepperd, M., Ying, S., Liu, J.: A General Software Defect-Proneness Prediction Framework. IEEE Trans. Softw. Eng. 37(3), 356\u2013370 (2011)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"519_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2020.110567","volume":"165","author":"Y Tan","year":"2020","unstructured":"Tan, Y., Xu, S., Wang, Z., Zhang, T., Xu, Z., Luo, X.: Bug severity prediction using question-and-answer pairs from stack overflow. J. Syst. Softw. 165, 110567 (2020)","journal-title":"J. Syst. Softw."},{"issue":"1","key":"519_CR27","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s13042-015-0328-7","volume":"8","author":"J Tanha","year":"2017","unstructured":"Tanha, J., Van Someren, M., Afsarmanesh, H.: Semi-supervised self-training for decision tree classifiers. Int. J. Mach. Learn. Cybernet. 8(1), 355\u2013370 (2017)","journal-title":"Int. J. Mach. Learn. Cybernet."},{"key":"519_CR28","doi-asserted-by":"crossref","unstructured":"Thung, F., Le, X.-B.D., Lo, D.: Active semi-supervised defect categorization. In: 2015 IEEE 23rd International Conference on Program Comprehension, pp. 60\u201370 (2015). IEEE","DOI":"10.1109\/ICPC.2015.15"},{"key":"519_CR29","doi-asserted-by":"crossref","unstructured":"Yang, C.-Z., Hou, C.-C., Kao, W.-C., Chen, X.: An empirical study on improving severity prediction of defect reports using feature selection. In: 2012 19th Asia-Pacific Software Engineering Conference, vol. 1, pp. 240\u2013249 (2012). IEEE","DOI":"10.1109\/APSEC.2012.144"},{"key":"519_CR30","doi-asserted-by":"crossref","unstructured":"Yu, Y., Zuo, S., Jiang, H., Ren, W., Zhao, T., Zhang, C.: Fine-tuning pre-trained language model with weak supervision: A contrastive-regularized self-training approach. arXiv:2010.07835 (2020)","DOI":"10.18653\/v1\/2021.naacl-main.84"},{"key":"519_CR31","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.jss.2016.02.034","volume":"117","author":"T Zhang","year":"2016","unstructured":"Zhang, T., Chen, J., Yang, G., Lee, B., Luo, X.: Towards more accurate severity prediction and fixer recommendation of software bugs. J. Syst. Softw. 117, 166\u2013184 (2016)","journal-title":"J. Syst. Softw."},{"key":"519_CR32","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Kumar, B., Wang, J.: Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 289\u2013305 (2018)","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"519_CR33","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Liu, X., Kumar, B., Wang, J.: Confidence regularized self-training. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5982\u20135991 (2019)","DOI":"10.1109\/ICCV.2019.00608"}],"container-title":["Automated Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-025-00519-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10515-025-00519-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-025-00519-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T13:57:39Z","timestamp":1757512659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10515-025-00519-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,19]]},"references-count":33,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["519"],"URL":"https:\/\/doi.org\/10.1007\/s10515-025-00519-3","relation":{},"ISSN":["0928-8910","1573-7535"],"issn-type":[{"type":"print","value":"0928-8910"},{"type":"electronic","value":"1573-7535"}],"subject":[],"published":{"date-parts":[[2025,5,19]]},"assertion":[{"value":"1 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2025","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 declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"52"}}