{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T10:30:04Z","timestamp":1780569004838,"version":"3.54.1"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"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":["Empir Software Eng"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s10664-024-10445-9","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T08:03:41Z","timestamp":1712563421000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["CoRT: Transformer-based code representations with self-supervision by predicting reserved words for code smell detection"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9353-7872","authenticated-orcid":false,"given":"Amal","family":"Alazba","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamoud","family":"Aljamaan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Alshayeb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,8]]},"reference":[{"key":"10445_CR1","doi-asserted-by":"publisher","unstructured":"Abdou A, Darwish N (2022) Severity classification of software code smells using machine learning techniques: A comparative study. J Softw Evol Process e2454. https:\/\/doi.org\/10.1002\/smr.2454","DOI":"10.1002\/smr.2454"},{"key":"10445_CR2","doi-asserted-by":"publisher","DOI":"10.1002\/smr.2320","volume":"33","author":"A AbuHassan","year":"2021","unstructured":"AbuHassan A, Alshayeb M, Ghouti L (2021) Software smell detection techniques: A systematic literature review. J Softw Evol Process 33:e2320. https:\/\/doi.org\/10.1002\/smr.2320","journal-title":"J Softw Evol Process"},{"key":"10445_CR3","doi-asserted-by":"publisher","unstructured":"Akiba T, Sano S, Yanase T, Ohta T, Koyama M (2019) Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery, New York, NY, USA, pp 2623\u20132631. https:\/\/doi.org\/10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"},{"key":"10445_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2021.106648","volume":"138","author":"A Alazba","year":"2021","unstructured":"Alazba A, Aljamaan H (2021) Code smell detection using feature selection and stacking ensemble: An empirical investigation. Inf Softw Technol 138:106648. https:\/\/doi.org\/10.1016\/j.infsof.2021.106648","journal-title":"Inf Softw Technol"},{"key":"10445_CR5","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s10664-023-10312-z","volume":"28","author":"A Alazba","year":"2023","unstructured":"Alazba A, Aljamaan H, Alshayeb M (2023) Deep learning approaches for bad smell detection: a systematic literature review. Empir Softw Eng 28:77. https:\/\/doi.org\/10.1007\/s10664-023-10312-z","journal-title":"Empir Softw Eng"},{"key":"10445_CR6","doi-asserted-by":"publisher","first-page":"3360","DOI":"10.1109\/ACCESS.2020.3047870","volume":"9","author":"T Alkhaeir","year":"2021","unstructured":"Alkhaeir T, Walter B (2021) The Effect of Code Smells on the Relationship Between Design Patterns and Defects. IEEE Access 9:3360\u20133373. https:\/\/doi.org\/10.1109\/ACCESS.2020.3047870","journal-title":"IEEE Access"},{"key":"10445_CR7","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1007\/s11219-018-9424-8","volume":"27","author":"K Alkharabsheh","year":"2019","unstructured":"Alkharabsheh K, Crespo Y, Manso E, Taboada JA (2019) Software Design Smell Detection: a systematic mapping study. Softw Qual J 27:1069\u20131148. https:\/\/doi.org\/10.1007\/s11219-018-9424-8","journal-title":"Softw Qual J"},{"key":"10445_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-019-04311-w","author":"A Al-Shaaby","year":"2020","unstructured":"Al-Shaaby A, Aljamaan H, Alshayeb M (2020) Bad Smell Detection Using Machine Learning Techniques: A Systematic Literature Review. Arab J Sci Eng. https:\/\/doi.org\/10.1007\/s13369-019-04311-w","journal-title":"Arab J Sci Eng"},{"key":"10445_CR9","doi-asserted-by":"publisher","unstructured":"Amorim L, Antunes N, Fonseca B, Ribeiro M (2015) Experience report: evaluating the effectiveness of decision trees for detecting code smells. In: 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE), pp 261\u2013269. https:\/\/doi.org\/10.1109\/ISSRE.2015.7381819","DOI":"10.1109\/ISSRE.2015.7381819"},{"key":"10445_CR10","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.knosys.2017.04.014","volume":"128","author":"F Arcelli Fontana","year":"2017","unstructured":"Arcelli Fontana F, Zanoni M (2017) Code smell severity classification using machine learning techniques. Knowl-Based Syst 128:43\u201358. https:\/\/doi.org\/10.1016\/j.knosys.2017.04.014","journal-title":"Knowl-Based Syst"},{"key":"10445_CR11","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1007\/s10664-015-9378-4","volume":"21","author":"F Arcelli Fontana","year":"2016","unstructured":"Arcelli Fontana F, M\u00e4ntyl\u00e4 MV, Zanoni M, Marino A (2016) Comparing and experimenting machine learning techniques for code smell detection. Empir Softw Eng 21:1143\u20131191. https:\/\/doi.org\/10.1007\/s10664-015-9378-4","journal-title":"Empir Softw Eng"},{"key":"10445_CR12","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1145\/163359.163375","volume":"36","author":"RD Banker","year":"1993","unstructured":"Banker RD, Datar SM, Kemerer CF, Zweig D (1993) Software complexity and maintenance costs. Commun ACM 36:81\u201394. https:\/\/doi.org\/10.1145\/163359.163375","journal-title":"Commun ACM"},{"key":"10445_CR13","doi-asserted-by":"publisher","unstructured":"Barbez A, Khomh F, Gu\u00e9h\u00e9neuc Y-G (2019a) A machine-learning based ensemble method for anti-patterns detection. J Syst Softw 161:110486. https:\/\/doi.org\/10.1016\/j.jss.2019.110486","DOI":"10.1016\/j.jss.2019.110486"},{"key":"10445_CR14","doi-asserted-by":"publisher","unstructured":"Barbez A, Khomh F, Gueheneuc Y-G (2019b) Deep Learning anti-patterns from Code metrics history. In: 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, Cleveland, OH, USA, pp 114\u2013124. https:\/\/doi.org\/10.1109\/ICSME.2019.00021","DOI":"10.1109\/ICSME.2019.00021"},{"key":"10445_CR15","doi-asserted-by":"publisher","unstructured":"Bryton S, Brito e Abreu F, Monteiro M (2010) Reducing subjectivity in code smells detection: experimenting with the long method. In: 2010 seventh international conference on the quality of information and communications technology. pp 337\u2013342. https:\/\/doi.org\/10.1109\/QUATIC.2010.60","DOI":"10.1109\/QUATIC.2010.60"},{"key":"10445_CR16","doi-asserted-by":"publisher","unstructured":"Charalampidou S, Ampatzoglou A, Avgeriou P (2015) Size and cohesion metrics as indicators of the long method bad smell: An empirical study. In: Proceedings of the 11th International Conference on Predictive Models and Data Analytics in Software Engineering. Association for Computing Machinery, Beijing, China, pp 1\u201310. https:\/\/doi.org\/10.1145\/2810146.2810155","DOI":"10.1145\/2810146.2810155"},{"key":"10445_CR17","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.infsof.2017.09.011","volume":"94","author":"Z Chen","year":"2018","unstructured":"Chen Z, Chen L, Ma W et al (2018) Understanding metric-based detectable smells in Python software: A comparative study. Inf Softw Technol 94:14\u201329. https:\/\/doi.org\/10.1016\/j.infsof.2017.09.011","journal-title":"Inf Softw Technol"},{"key":"10445_CR18","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of naacL-HLT, vol 1, p 2. https:\/\/doi.org\/10.18653\/V1\/N19-1423","DOI":"10.18653\/V1\/N19-1423"},{"key":"10445_CR19","doi-asserted-by":"publisher","first-page":"162869","DOI":"10.1109\/ACCESS.2021.3133810","volume":"9","author":"S Dewangan","year":"2021","unstructured":"Dewangan S, Rao RS, Mishra A, Gupta M (2021) A novel approach for code smell detection: An empirical study. IEEE Access 9:162869\u2013162883.\u00a0https:\/\/doi.org\/10.1109\/ACCESS.2021.3133810","journal-title":"IEEE Access"},{"key":"10445_CR20","doi-asserted-by":"publisher","unstructured":"Di Nucci D, Palomba F, Tamburri DA, Serebrenik A, De Lucia A (2018) Detecting code smells using machine learning techniques: Are we there yet? 2018 IEEE 25th Int Conf Softw Anal Evol Reengineering SANER 612\u2013621. https:\/\/doi.org\/10.1109\/SANER.2018.8330266","DOI":"10.1109\/SANER.2018.8330266"},{"key":"10445_CR21","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, Abreu FB e, Carneiro G de F (2022) Crowdsmelling: A preliminary study on using collective knowledge in code smells detection. Empir Softw Eng 27:69. https:\/\/doi.org\/10.1007\/s10664-021-10110-5","journal-title":"Empir Softw Eng"},{"key":"10445_CR22","doi-asserted-by":"publisher","unstructured":"Feng Z, Guo D, Tang D, Duan N, Feng X, Gong M, Shou L, Qin B, Liu T, Jiang D, Zhou M (2020) CodeBERT: A pre-trained model for programming and natural languages. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online. Association for Computational Linguistics, pp 1536\u20131547. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.139","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"10445_CR23","doi-asserted-by":"publisher","unstructured":"Fontana FA, Zanoni M, Marino A, M\u00e4ntyl\u00e4 MV (2013) Code smell detection: Towards a machine learning-based approach. In: Proceedings of the 2013 IEEE international conference on software maintenance. IEEE Computer Society, USA, pp 396\u2013399. https:\/\/doi.org\/10.1109\/ICSM.2013.56","DOI":"10.1109\/ICSM.2013.56"},{"key":"10445_CR24","volume-title":"Refactoring: Improving the Design of Existing Code","author":"M Fowler","year":"1999","unstructured":"Fowler M, Beck K, Brant J et al (1999) Refactoring: Improving the design of existing code, 1st edn. Addison-Wesley Professional, Reading, MA","edition":"1"},{"key":"10445_CR25","unstructured":"Gidaris S, Singh P, Komodakis N (2018) Unsupervised representation learning by predicting image rotations. ArXiv, abs\/1803.07728."},{"key":"10445_CR26","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1007\/s11219-020-09498-y","volume":"28","author":"T Guggulothu","year":"2020","unstructured":"Guggulothu T, Moiz SA (2020) Code smell detection using multi-label classification approach. Softw Qual J 28:1063\u20131086. https:\/\/doi.org\/10.1007\/s11219-020-09498-y","journal-title":"Softw Qual J"},{"key":"10445_CR27","doi-asserted-by":"publisher","unstructured":"Guo X, Shi C, Jiang H (2019) Deep semantic-based feature envy identification. In: Proceedings of the 11th Asia-Pacific Symposium on Internetware. Association for Computing Machinery, New York, NY, USA, pp 1\u20136. https:\/\/doi.org\/10.1145\/3361242.3361257","DOI":"10.1145\/3361242.3361257"},{"key":"10445_CR28","unstructured":"Guo D, Ren S, Lu S, Feng Z, Tang D, Liu S, Zhou L, Duan N, Yin J, Jiang D, Zhou M (2020) GraphCodeBERT: Pre-training Code Representations with Data Flow. ArXiv, abs\/2009.08366"},{"key":"10445_CR29","doi-asserted-by":"publisher","unstructured":"Guo D, Lu S, Duan N, Wang Y, Zhou M, Yin J (2022) UniXcoder: Unified cross-modal pre-training for code representation. Annual Meeting of the Association for Computational Linguistics. https:\/\/doi.org\/10.48550\/arXiv.2203.03850","DOI":"10.48550\/arXiv.2203.03850"},{"key":"10445_CR30","doi-asserted-by":"publisher","unstructured":"Hadj-Kacem M, Bouassida N (2018) A hybrid approach to detect code smells using deep learning. In: Proceedings of the 13th international conference on evaluation of novel approaches to software engineering. SCITEPRESS - Science and Technology Publications, Lda, Setubal, PRT, pp 137\u2013146. https:\/\/doi.org\/10.5220\/0006709801370146","DOI":"10.5220\/0006709801370146"},{"key":"10445_CR31","doi-asserted-by":"publisher","unstructured":"Hadj-Kacem M, Bouassida N (2019a) Deep representation learning for code smells detection using variational auto-encoder. In: 2019 international joint conference on neural networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN.2019.8851854","DOI":"10.1109\/IJCNN.2019.8851854"},{"key":"10445_CR32","doi-asserted-by":"crossref","unstructured":"Hadj-Kacem M, Bouassida N (2019b) Improving the identification of code smells by combining structural and semantic information. In: Gedeon T, Wong KW, Lee M (eds) Neural Information Processing. Springer International Publishing, Cham, pp 296\u2013304","DOI":"10.1007\/978-3-030-36808-1_32"},{"key":"10445_CR33","doi-asserted-by":"publisher","unstructured":"Hassaine S, Khomh F, Gueheneuc Y-G, Hamel S (2010) IDS: an immune-inspired approach for the detection of software design smells. In: 2010 Seventh International Conference on the Quality of Information and Communications Technology, pp 343\u2013348. https:\/\/doi.org\/10.1109\/QUATIC.2010.61","DOI":"10.1109\/QUATIC.2010.61"},{"key":"10445_CR34","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10445_CR35","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1109\/TR.2020.3001918","volume":"70","author":"W Hua","year":"2021","unstructured":"Hua W, Sui Y, Wan Y et al (2021) FCCA: Hybrid Code Representation for Functional Clone Detection Using Attention Networks. IEEE Trans Reliab 70:304\u2013318. https:\/\/doi.org\/10.1109\/TR.2020.3001918","journal-title":"IEEE Trans Reliab"},{"key":"10445_CR36","unstructured":"Ioffe S, Szegedy C (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In International conference on machine learning, pp 448\u2013456"},{"key":"10445_CR37","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/technologies9010002","volume":"9","author":"A Jaiswal","year":"2021","unstructured":"Jaiswal A, Babu AR, Zadeh MZ et al (2021) A survey on contrastive self-supervised learning. Technologies 9:2. https:\/\/doi.org\/10.3390\/technologies9010002","journal-title":"Technologies"},{"key":"10445_CR38","doi-asserted-by":"publisher","unstructured":"Kaur A, Jain S, Goel S (2017) A support vector machine based approach for code smell detection. In: 2017 international conference on machine learning and data science (MLDS), pp 9\u201314. https:\/\/doi.org\/10.1109\/MLDS.2017.8","DOI":"10.1109\/MLDS.2017.8"},{"key":"10445_CR39","doi-asserted-by":"publisher","first-page":"1725","DOI":"10.11591\/ijeecs.v26.i3.pp1725-1735","volume":"26","author":"NAA Khleel","year":"2022","unstructured":"Khleel NAA, Neh\u00e9z K (2022) Deep convolutional neural network model for bad code smells detection based on oversampling method. Indones J Electr Eng Comput Sci 26:1725\u20131735. https:\/\/doi.org\/10.11591\/ijeecs.v26.i3.pp1725-1735","journal-title":"Indones J Electr Eng Comput Sci"},{"key":"10445_CR40","doi-asserted-by":"publisher","unstructured":"Khomh F, Vaucher S, Gu\u00e9h\u00e9neuc Y-G, Sahraoui H (2009) A Bayesian approach for the detection of code and design smells. In: 2009 Ninth International Conference on Quality Software, pp 305\u2013314. https:\/\/doi.org\/10.1109\/QSIC.2009.47","DOI":"10.1109\/QSIC.2009.47"},{"key":"10445_CR41","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.jss.2010.11.921","volume":"84","author":"F Khomh","year":"2011","unstructured":"Khomh F, Vaucher S, Gu\u00e9h\u00e9neuc Y-G, Sahraoui H (2011) BDTEX: A GQM-based Bayesian approach for the detection of antipatterns. J Syst Softw 84:559\u2013572. https:\/\/doi.org\/10.1016\/j.jss.2010.11.921","journal-title":"J Syst Softw"},{"key":"10445_CR42","doi-asserted-by":"publisher","unstructured":"Kim DK (2017) Finding bad code smells with neural network models. Int J Electr Comput Eng IJECE 7:3613\u20133621. https:\/\/doi.org\/10.11591\/ijece.v7i6.pp3613-3621","DOI":"10.11591\/ijece.v7i6.pp3613-3621"},{"key":"10445_CR43","unstructured":"Kotsiantis SB (2007) Supervised machine learning: A review of classification techniques. In: Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies. IOS Press, NLD, pp 3\u201324"},{"key":"10445_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2020.110610","volume":"167","author":"G Lacerda","year":"2020","unstructured":"Lacerda G, Petrillo F, Pimenta M, Gu\u00e9h\u00e9neuc YG (2020) Code smells and refactoring: A tertiary systematic review of challenges and observations. J Syst Softw 167:110610. https:\/\/doi.org\/10.1016\/j.jss.2020.110610","journal-title":"J Syst Softw"},{"key":"10445_CR45","unstructured":"Le H, Wang Y, Gotmare AD, Savarese S, Hoi SC (2022) Coderl: Mastering code generation through pretrained models and deep reinforcement learning.\u00a0Adv Neural Inf Process Syst 35:21314\u201321328"},{"key":"10445_CR46","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1023\/A:1007608224229","volume":"40","author":"T-S Lim","year":"2000","unstructured":"Lim T-S, Loh W-Y, Shih Y-S (2000) A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach Learn 40:203\u2013228. https:\/\/doi.org\/10.1023\/A:1007608224229","journal-title":"Mach Learn"},{"key":"10445_CR47","doi-asserted-by":"publisher","unstructured":"Liu H, Xu Z, Zou Y (2018) Deep learning based feature envy detection. In: Proceedings of the 33rd ACM\/IEEE International Conference on Automated Software Engineering. ACM, New York, NY, USA, pp 385\u2013396. https:\/\/doi.org\/10.1145\/3238147.3238166","DOI":"10.1145\/3238147.3238166"},{"key":"10445_CR48","doi-asserted-by":"publisher","unstructured":"Liu H, Jin J, Xu Z, Zou Y, Bu Y, Zhang L (2019) Deep learning based code smell detection. IEEE Trans Softw Eng 47(9):1811\u20131837. https:\/\/doi.org\/10.1109\/TSE.2019.2936376","DOI":"10.1109\/TSE.2019.2936376"},{"key":"10445_CR49","doi-asserted-by":"publisher","unstructured":"Liu X, Zhang F, Hou Z, Mian L, Wang Z, Zhang J, Tang J (2021) Self-supervised Learning: Generative or Contrastive.\u00a0IEEE Trans Knowl Data Eng\u00a035(1):857\u2013876. https:\/\/doi.org\/10.1109\/TKDE.2021.3090866","DOI":"10.1109\/TKDE.2021.3090866"},{"key":"10445_CR50","doi-asserted-by":"crossref","unstructured":"Liu S, Wu B, Xie X, Meng G, Liu Y (2023) ContraBERT: Enhancing code pre-trained models via contrastive learning. arXiv preprint arXiv:2301.09072","DOI":"10.1109\/ICSE48619.2023.00207"},{"key":"10445_CR51","unstructured":"Lu S, Guo D, Ren S, Huang J, Svyatkovskiy A, Blanco A, Clement C, Drain D, Jiang D, Tang D, Li G (2021) CodeXGLUE: A machine learning benchmark dataset for code understanding and generation. arXiv preprint arXiv:2102.04664"},{"key":"10445_CR52","doi-asserted-by":"publisher","unstructured":"Maiga A, Ali N, Bhattacharya N, Saban\u00e9 A, Gu\u00e9h\u00e9neuc YG, Aimeur E (2012a) SMURF: a SVM-based incremental anti-pattern detection approach. In: 2012 19th Working Conference on Reverse Engineering, pp 466\u2013475. https:\/\/doi.org\/10.1109\/WCRE.2012.56","DOI":"10.1109\/WCRE.2012.56"},{"key":"10445_CR53","doi-asserted-by":"publisher","unstructured":"Maiga A, Ali N, Bhattacharya N, Saban\u00e9 A, Gu\u00e9h\u00e9neuc YG, Antoniol G, Aimeur E (2012b) Support vector machines for anti-pattern detection. In: 2012 Proceedings of the 27th IEEE\/ACM International Conference on Automated Software Engineering, pp 278\u2013281. https:\/\/doi.org\/10.1145\/2351676.2351723","DOI":"10.1145\/2351676.2351723"},{"key":"10445_CR54","doi-asserted-by":"publisher","DOI":"10.1002\/smr.2255","volume":"32","author":"BB Mayvan","year":"2020","unstructured":"Mayvan BB, Rasoolzadegan A, Jafari AJ (2020) Bad smell detection using quality metrics and refactoring opportunities. J Softw Evol Process 32:e2255. https:\/\/doi.org\/10.1002\/smr.2255","journal-title":"J Softw Evol Process"},{"key":"10445_CR55","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/TSE.2009.50","volume":"36","author":"N Moha","year":"2010","unstructured":"Moha N, Gueheneuc Y-G, Duchien L, Le Meur A-F (2010) DECOR: A Method for the Specification and Detection of Code and Design Smells. IEEE Trans Softw Eng 36:20\u201336. https:\/\/doi.org\/10.1109\/TSE.2009.50","journal-title":"IEEE Trans Softw Eng"},{"key":"10445_CR56","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1006\/jmps.1999.1283","volume":"44","author":"IJ Myung","year":"2000","unstructured":"Myung IJ (2000) The Importance of Complexity in Model Selection. J Math Psychol 44:190\u2013204. https:\/\/doi.org\/10.1006\/jmps.1999.1283","journal-title":"J Math Psychol"},{"key":"10445_CR57","doi-asserted-by":"publisher","unstructured":"Nafi KW, Kar TS, Roy B, Roy CK, Schneider KA (2019) CLCDSA: cross language code clone detection using syntactical features and api documentation. In: 2019 34th IEEE\/ACM International Conference on Automated Software Engineering (ASE). IEEE, San Diego, CA, USA, pp 1026\u20131037. https:\/\/doi.org\/10.1109\/ASE.2019.00099","DOI":"10.1109\/ASE.2019.00099"},{"key":"10445_CR58","doi-asserted-by":"publisher","unstructured":"Olbrich SM, Cruzes DS, Sj\u00f8berg DIK (2010) Are all code smells harmful? A study of God Classes and Brain Classes in the evolution of three open source systems. In: 2010 IEEE International Conference on Software Maintenance, pp 1\u201310. https:\/\/doi.org\/10.1109\/ICSM.2010.5609564","DOI":"10.1109\/ICSM.2010.5609564"},{"key":"10445_CR59","unstructured":"Parr T (2013) The definitive ANTLR 4 reference. The Definitive ANTLR 4 Reference, pp 1\u2013326"},{"key":"10445_CR60","doi-asserted-by":"publisher","unstructured":"Ren S, Shi C, Zhao S (2021) Exploiting multi-aspect interactions for god class detection with dataset fine-tuning. In: 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, Madrid, Spain, pp 864\u2013873. https:\/\/doi.org\/10.1109\/COMPSAC51774.2021.00119","DOI":"10.1109\/COMPSAC51774.2021.00119"},{"key":"10445_CR61","unstructured":"Roy GG, Veraart VE (1996) Software engineering education: from an engineering perspective. In: Proceedings 1996 International Conference Software Engineering: Education and Practice, pp 256\u2013262"},{"key":"10445_CR62","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1370","volume":"9","author":"R Sandouka","year":"2023","unstructured":"Sandouka R, Aljamaan H (2023) Python code smells detection using conventional machine learning models. PeerJ Comput Sci 9:e1370. https:\/\/doi.org\/10.7717\/peerj-cs.1370","journal-title":"PeerJ Comput Sci"},{"key":"10445_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2021.110936","volume":"176","author":"T Sharma","year":"2021","unstructured":"Sharma T, Efstathiou V, Louridas P, Spinellis D (2021) Code smell detection by deep direct-learning and transfer-learning. J Syst Softw 176:110936. https:\/\/doi.org\/10.1016\/j.jss.2021.110936","journal-title":"J Syst Softw"},{"key":"10445_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105240","volume":"115","author":"B Sotto-Mayor","year":"2022","unstructured":"Sotto-Mayor B, Elmishali A, Kalech M, Abreu R (2022) Exploring Design smells for smell-based defect prediction. Eng Appl Artif Intell 115:105240. https:\/\/doi.org\/10.1016\/j.engappai.2022.105240","journal-title":"Eng Appl Artif Intell"},{"key":"10445_CR65","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1109\/TSE.2018.2794977","volume":"45","author":"C Tantithamthavorn","year":"2019","unstructured":"Tantithamthavorn C, McIntosh S, Hassan AE, Matsumoto K (2019) The Impact of Automated Parameter Optimization on Defect Prediction Models. IEEE Trans Softw Eng 45:683\u2013711. https:\/\/doi.org\/10.1109\/TSE.2018.2794977","journal-title":"IEEE Trans Softw Eng"},{"key":"10445_CR66","doi-asserted-by":"publisher","unstructured":"Tempero E, Anslow C, Dietrich J, Han T, Li J, Lumpe M, Melton H, Noble J (2010) The qualitas corpus: A curated collection of java code for empirical studies. In: 2010 Asia Pacific Software Engineering Conference, pp 336\u2013345. https:\/\/doi.org\/10.1109\/APSEC.2010.46","DOI":"10.1109\/APSEC.2010.46"},{"key":"10445_CR67","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention Is All You Need.\u00a0Adv Neural Inf Process Syst 30"},{"key":"10445_CR68","doi-asserted-by":"publisher","unstructured":"Wang X, Dang Y, Zhang L, Zhang D, Lan E, Mei H (2012) Can I clone this piece of code here? In: 2012 Proceedings of the 27th IEEE\/ACM International Conference on Automated Software Engineering, pp 170\u2013179. https:\/\/doi.org\/10.1145\/2351676.2351701","DOI":"10.1145\/2351676.2351701"},{"key":"10445_CR69","doi-asserted-by":"publisher","unstructured":"Wang H, Liu J, Kang J, Yin W, Sun H, Wang H (2020) Feature envy detection based on Bi-LSTM with self-attention mechanism. In: 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA\/BDCloud\/SocialCom\/SustainCom). IEEE, Exeter, United Kingdom, pp 448\u2013457. https:\/\/doi.org\/10.1109\/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00082","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00082"},{"key":"10445_CR70","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang W, Joty S, Hoi SCH (2021) CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. arXiv preprint arXiv:2109.00859","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"10445_CR71","doi-asserted-by":"crossref","unstructured":"Wang Y, Le H, Gotmare AD, Bui ND, Li J, Hoi SC (2023) CodeT5+: Open code large language models for code understanding and generation. arXiv preprint arXiv:2305.07922","DOI":"10.18653\/v1\/2023.emnlp-main.68"},{"key":"10445_CR72","doi-asserted-by":"crossref","unstructured":"Watanabe S, Hutter F (2022) c-TPE: Generalizing tree-structured parzen estimator with inequality constraints for continuous and categorical hyperparameter optimization. arXiv preprint arXiv:2211.14411","DOI":"10.24963\/ijcai.2023\/486"},{"key":"10445_CR73","doi-asserted-by":"publisher","unstructured":"White M, Tufano M, Vendome C, Poshyvanyk D (2016) Deep learning code fragments for code clone detection. In: 2016 31st IEEE\/ACM International Conference on Automated Software Engineering (ASE), pp 87\u201398. https:\/\/doi.org\/10.1145\/2970276.2970326","DOI":"10.1145\/2970276.2970326"},{"key":"10445_CR74","doi-asserted-by":"crossref","unstructured":"Xu W, Zhang X (2021) Multi-granularity code smell detection using deep learning method based on abstract syntax tree.  In: Proceeding 33rd Int. Conf. Software Engineering and Knowledge Engineering, pp 503\u2013509","DOI":"10.18293\/SEKE2021-014"},{"key":"10445_CR75","doi-asserted-by":"publisher","unstructured":"Yin X, Shi C, Zhao S (2021) Local and global feature based explainable feature envy detection. In: 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, Madrid, Spain, pp 942\u2013951. https:\/\/doi.org\/10.1109\/COMPSAC51774.2021.00127","DOI":"10.1109\/COMPSAC51774.2021.00127"}],"container-title":["Empirical Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-024-10445-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10664-024-10445-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-024-10445-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T02:07:46Z","timestamp":1717207666000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10664-024-10445-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,8]]},"references-count":75,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["10445"],"URL":"https:\/\/doi.org\/10.1007\/s10664-024-10445-9","relation":{},"ISSN":["1382-3256","1573-7616"],"issn-type":[{"value":"1382-3256","type":"print"},{"value":"1573-7616","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,8]]},"assertion":[{"value":"8 January 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2024","order":2,"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 conflict of interest relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interests\/Competing Interests"}}],"article-number":"59"}}