{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T13:02:22Z","timestamp":1777899742213,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"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":["Int J Syst Assur Eng Manag"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s13198-024-02326-7","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T13:02:09Z","timestamp":1713963729000},"page":"3210-3224","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing software code smell detection with modified cost-sensitive SVM"],"prefix":"10.1007","volume":"15","author":[{"given":"Praveen Singh","family":"Thakur","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahipal","family":"Jadeja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0280-7364","authenticated-orcid":false,"given":"Satyendra Singh","family":"Chouhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,24]]},"reference":[{"key":"2326_CR1","unstructured":"Abdou M, Nasir A, Neelesh B, Aminata S, Yann-Ga\u00ebl G, Giuliano A, Esma A (2012) Support vector machines for anti-pattern detection. In Proceedings of the 27th IEEE\/ACM international conference on automated software engineering, pages 278\u2013281,"},{"key":"2326_CR2","unstructured":"Arcelli FF, Marco Z, Alessandro M, Mika\u00a0VM (2013). Code smell detection: towards a machine learning-based approach. In 2013 IEEE international conference on software maintenance, pages 396\u2013399. IEEE,"},{"key":"2326_CR3","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.knosys.2017.04.014","volume":"128","author":"FF Arcelli","year":"2017","unstructured":"Arcelli FF, Marco Z (2017) Code smell severity classification using machine learning techniques. Knowl-Based Syst 128:43\u201358","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"2326_CR4","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1007\/s10664-015-9378-4","volume":"21","author":"FF Arcelli","year":"2016","unstructured":"Arcelli FF, M\u00e4ntyl\u00e4 MV, Marco Z, Alessandro M (2016) Comparing and experimenting machine learning techniques for code smell detection. Empir Softw Eng 21(3):1143\u20131191","journal-title":"Empir Softw Eng"},{"key":"2326_CR5","unstructured":"Carlos MR, Yania C (2005). Parallel inheritance hierarchy: detection from a static view of the system. In 6th international workshop on object oriented reenginering (WOOR), Glasgow, UK, page\u00a06,"},{"key":"2326_CR6","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, Philip KW (2002) Synthetic minority over-sampling technique Smote. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"issue":"1","key":"2326_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-017-0155-3","volume":"10","author":"D Chicco","year":"2017","unstructured":"Chicco D (2017) Ten quick tips for machine learning in computational biology. BioData Min 10(1):1\u201317","journal-title":"BioData Min"},{"key":"2326_CR8","unstructured":"Ciupke Oliver (1999). Automatic detection of design problems in object-oriented reengineering. In Proceedings of technology of object-oriented languages and systems-TOOLS 30 (Cat. No. PR00278), pages 18\u201332. IEEE,"},{"key":"2326_CR9","unstructured":"Davide S, Fabio P, Andy Z, Magiel B, Alberto B (2018). On the relation of test smells to software code quality. In 2018 IEEE international conference on software maintenance and evolution (ICSME), pages 1\u201312. IEEE,"},{"key":"2326_CR10","doi-asserted-by":"crossref","unstructured":"De\u00a0Manuel S, Fabiano P, Fabio P, De\u00a0Andrea L (2021). Comparing within-and cross-project machine learning algorithms for code smell detection. In Proceedings of the 5th international workshop on machine learning techniques for software quality evolution, pages 1\u20136,","DOI":"10.1145\/3472674.3473978"},{"issue":"3","key":"2326_CR11","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10664-021-10110-5","volume":"27","author":"JP dos Reis","year":"2022","unstructured":"dos Reis JP, Fernando BA, de Glauco FC (2022) Crowdsmelling: a preliminary study on using collective knowledge in code smells detection. Emp Softw Eng 27(3):69","journal-title":"Emp Softw Eng"},{"key":"2326_CR12","doi-asserted-by":"crossref","unstructured":"Eduardo F, Johnatan O, Gustavo V, Thanis P, Eduardo F (2016). A review-based comparative study of bad smell detection tools. In Proceedings of the 20th international conference on evaluation and assessment in software engineering, pages 1\u201312,","DOI":"10.1145\/2915970.2915984"},{"key":"2326_CR13","doi-asserted-by":"crossref","unstructured":"Emerson MH, Black Andrew\u00a0P (2010). An interactive ambient visualization for code smells. In Proceedings of the 5th international symposium on Software visualization, pages 5\u201314,","DOI":"10.1145\/1879211.1879216"},{"key":"2326_CR14","doi-asserted-by":"crossref","unstructured":"Fabiano P, Di\u00a0Nucci D, De\u00a0Coen R, De\u00a0Andrea L (2019). On the role of data balancing for machine learning-based code smell detection. In Proceedings of the 3rd ACM SIGSOFT international workshop on machine learning techniques for software quality evaluation, pages 19\u201324,","DOI":"10.1145\/3340482.3342744"},{"key":"2326_CR15","unstructured":"Fabiano P, Fabio P, Di\u00a0Dario N, De\u00a0Andrea L (2019). Comparing heuristic and machine learning approaches for metric-based code smell detection. In 2019 IEEE\/ACM 27th international conference on program comprehension (ICPC), pages 93\u2013104. IEEE,"},{"key":"2326_CR16","doi-asserted-by":"crossref","unstructured":"Haibo H, Yang B, Garcia EA, Shutao L (2008). Adasyn: adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), pages 1322\u20131328. IEEE,","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"2326_CR17","unstructured":"Inderjeet M, Zhang I (2003). knn approach to unbalanced data distributions: a case study involving information extraction. In Proceedings of workshop on learning from imbalanced datasets, volume 126, pages 1\u20137. ICML,"},{"issue":"5","key":"2326_CR18","first-page":"2701","volume":"14","author":"N Jatin","year":"2022","unstructured":"Jatin N, Kumar CJ (2022) Sshm: smote-stacked hybrid model for improving severity classification of code smell. Int J Inform Technol 14(5):2701\u20132707","journal-title":"Int J Inform Technol"},{"key":"2326_CR19","doi-asserted-by":"crossref","unstructured":"Kaur Amandeep, Jain Sushma, Goel Shivani (2017). A support vector machine based approach for code smell detection. In 2017 International conference on machine learning and data science (MLDS), pages 9\u201314. IEEE,","DOI":"10.1109\/MLDS.2017.8"},{"issue":"1999","key":"2326_CR20","first-page":"75","volume":"1","author":"B Kent","year":"1999","unstructured":"Kent B, Martin F, Grandma B (1999) Bad smells in code. Refactoring Improv Des Exist Code 1(1999):75\u201388","journal-title":"Refactoring Improv Des Exist Code"},{"issue":"9","key":"2326_CR21","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TSE.2014.2331057","volume":"40","author":"W Kessentini","year":"2014","unstructured":"Kessentini W, Kessentini M, Sahraoui H, Bechikh S, Ouni A (2014) A cooperative parallel search-based software engineering approach for code-smells detection. IEEE Trans Softw Eng 40(9):841\u2013861","journal-title":"IEEE Trans Softw Eng"},{"key":"2326_CR22","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s10664-011-9171-y","volume":"17","author":"F Khomh","year":"2012","unstructured":"Khomh F, Di Penta M, Gu\u00e9h\u00e9neuc Y-G, Antoniol G (2012) An exploratory study of the impact of antipatterns on class change-and fault-proneness. Empir Softw Eng 17:243\u2013275","journal-title":"Empir Softw Eng"},{"key":"2326_CR23","unstructured":"Konstantinos V, Colin C, Nello C et\u00a0al (1999). Controlling the sensitivity of support vector machines. In Proceedings of the international joint conference on AI, volume\u00a055, page\u00a060. Stockholm,"},{"issue":"4","key":"2326_CR24","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.entcs.2005.02.059","volume":"141","author":"J Kreimer","year":"2005","unstructured":"Kreimer J (2005) Adaptive detection of design flaws. Electron Notes Theor Comput Sci 141(4):117\u2013136","journal-title":"Electron Notes Theor Comput Sci"},{"key":"2326_CR25","unstructured":"Lei S, Wangshu L, Xiang C, Qing G, Xuejun L (2020). Improving machine learning-based code smell detection via hyper-parameter optimization. In 2020 27th Asia-pacific software engineering conference (APSEC), pages 276\u2013285. IEEE,"},{"issue":"1","key":"2326_CR26","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1109\/TSE.2011.9","volume":"38","author":"H Liu","year":"2011","unstructured":"Liu H, Ma Z, Shao W, Niu Z (2011) Schedule of bad smell detection and resolution: a new way to save effort. IEEE Trans Softw Eng 38(1):220\u2013235","journal-title":"IEEE Trans Softw Eng"},{"key":"2326_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2022.107112","volume":"155","author":"L Madeyski","year":"2023","unstructured":"Madeyski L, Lewowski T (2023) Detecting code smells using industry-relevant data. Inf Softw Technol 155:107112","journal-title":"Inf Softw Technol"},{"key":"2326_CR28","unstructured":"Marios F, Nikolaos T, Alexander C (2007). Jdeodorant: identification and removal of feature envy bad smells. In 2007 IEEE international conference on software maintenance, pages 519\u2013520. IEEE,"},{"key":"2326_CR29","unstructured":"Nakarin M, Pomsiri M (2011). Bad-smell prediction from software design model using machine learning techniques. In 2011 Eighth international joint conference on computer science and software engineering (JCSSE), pages 331\u2013336. IEEE,"},{"issue":"1","key":"2326_CR30","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1504\/IJKESDP.2011.039875","volume":"3","author":"M Nguyen Hien","year":"2011","unstructured":"Nguyen Hien M, Cooper Eric W, Katsuari K (2011) Borderline over-sampling for imbalanced data classification. Int J Knowl Eng Soft Data Parad 3(1):4\u201321","journal-title":"Int J Knowl Eng Soft Data Parad"},{"key":"2326_CR31","unstructured":"Paris A, Philippe K, Ipek O, Carolyn S (2016) Managing technical debt in software engineering (dagstuhl seminar 16162). In Dagstuhl reports, volume\u00a06. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik,"},{"issue":"3","key":"2326_CR32","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1109\/TIT.1968.1054155","volume":"14","author":"H Peter","year":"1968","unstructured":"Peter H (1968) The condensed nearest neighbor rule (corresp.). IEEE Transact Inform Theory 14(3):515\u2013516","journal-title":"IEEE Transact Inform Theory"},{"issue":"20","key":"2326_CR33","doi-asserted-by":"publisher","first-page":"10321","DOI":"10.3390\/app122010321","volume":"12","author":"SRR SeemaD","year":"2022","unstructured":"SeemaD SRR, Alok M, Manjari G (2022) Code smell detection using ensemble machine learning algorithms. Appl Sci 12(20):10321","journal-title":"Appl Sci"},{"key":"2326_CR34","unstructured":"Stephane V, Foutse K, Naouel M, Yann-Ga\u00ebl G (2009). Tracking design smells: lessons from a study of god classes. In 2009 16th working conference on reverse engineering, pages 145\u2013154. IEEE,"},{"key":"2326_CR35","doi-asserted-by":"crossref","unstructured":"Tao L, Xue F, Fu C, Luqun L (2021). A novel approach for code smells detection based on deep leaning. In Applied cryptography in computer and communications: first EAI international conference, AC3 2021, Virtual Event, May 15-16, 2021, Proceedings 1, pages 171\u2013174. Springer,","DOI":"10.1007\/978-3-030-80851-8_12"},{"key":"2326_CR36","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1007\/s11219-020-09498-y","volume":"28","author":"G Thirupathi","year":"2020","unstructured":"Thirupathi G, Abdul MS (2020) Code smell detection using multi-label classification approach. Softw Qual J 28:1063\u20131086","journal-title":"Softw Qual J"},{"key":"2326_CR37","unstructured":"Tim M, Andrian M (2008). Automated severity assessment of software defect reports. In 2008 IEEE international conference on software maintenance, pages 346\u2013355. IEEE,"},{"key":"2326_CR38","unstructured":"Tomek Ivan (1976). Two modifications of cnn"},{"key":"2326_CR39","doi-asserted-by":"crossref","unstructured":"Tushar S, Vasiliki E, Panos L, Diomidis S (2021). Code smell detection by deep direct-learning and transfer-learning. J Syst Softw, 176,","DOI":"10.1016\/j.jss.2021.110936"},{"key":"2326_CR40","unstructured":"Umberto A, Arcelli FF, Marco Z (2018). Poster: machine learning based code smell detection through wekanose. In 2018 IEEE\/ACM 40th international conference on software engineering: companion proceedings (ICSE-Companion), pages 288\u2013289. IEEE,"},{"key":"2326_CR41","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","volume":"3","author":"L Wilson Dennis","year":"1972","unstructured":"Wilson Dennis L (1972) Asymptotic properties of nearest neighbor rules using edited data. IEEE Transact Syst Man Cybern 3:408\u2013421","journal-title":"IEEE Transact Syst Man Cybern"},{"key":"2326_CR42","unstructured":"Yang Z, Chunhao D (2021). Mars: detecting brain class\/method code smell based on metric\u2013attention mechanism and residual network. J Softw Evol Process, page e2403,"},{"key":"2326_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109737","volume":"255","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Ge C, Hong S, Tian R, Dong C, Liu J (2022) Delesmell: Code smell detection based on deep learning and latent semantic analysis. Knowl-Based Syst 255:109737","journal-title":"Knowl-Based Syst"}],"container-title":["International Journal of System Assurance Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-024-02326-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13198-024-02326-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-024-02326-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T23:23:10Z","timestamp":1731799390000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13198-024-02326-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,24]]},"references-count":43,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["2326"],"URL":"https:\/\/doi.org\/10.1007\/s13198-024-02326-7","relation":{},"ISSN":["0975-6809","0976-4348"],"issn-type":[{"value":"0975-6809","type":"print"},{"value":"0976-4348","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,24]]},"assertion":[{"value":"6 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2024","order":4,"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 potential Conflict 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 article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and\/or animals participants"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}