{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T01:18:29Z","timestamp":1778721509896,"version":"3.51.4"},"reference-count":130,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"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":[[2023,5]]},"DOI":"10.1007\/s10664-023-10312-z","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T10:38:30Z","timestamp":1683801510000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Deep learning approaches for bad smell detection: a systematic literature review"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9353-7872","authenticated-orcid":false,"given":"Amal","family":"Alazba","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamoud","family":"Aljamaan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Alshayeb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,11]]},"reference":[{"issue":"3","key":"10312_CR1","doi-asserted-by":"publisher","first-page":"e2320","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(3):e2320. https:\/\/doi.org\/10.1002\/smr.2320","journal-title":"J Softw Evol Process"},{"issue":"3","key":"10312_CR2","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. Software Qual J 27(3):1069\u20131148. https:\/\/doi.org\/10.1007\/s11219-018-9424-8","journal-title":"Software Qual J"},{"key":"10312_CR3","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":"10312_CR4","unstructured":"Anne-Wil Harzing (2006) Publish or perish. Harzing.Com. Retrieved January 23, 2022, from https:\/\/harzing.com\/resources\/publish-or-perish"},{"key":"10312_CR5","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.neucom.2021.08.010","volume":"463","author":"P Ardimento","year":"2021","unstructured":"Ardimento P, Aversano L, Bernardi ML, Cimitile M, Iammarino M (2021) Temporal convolutional networks for just-in-time design smells prediction using fine-grained software metrics. Neurocomputing 463:454\u2013471. https:\/\/doi.org\/10.1016\/j.neucom.2021.08.010","journal-title":"Neurocomputing"},{"key":"10312_CR6","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.infsof.2018.12.009","volume":"108","author":"MI Azeem","year":"2019","unstructured":"Azeem MI, Palomba F, Shi L, Wang Q (2019) Machine learning techniques for code smell detection: A systematic literature review and meta-analysis. Inf Softw Technol 108:115\u2013138. https:\/\/doi.org\/10.1016\/j.infsof.2018.12.009","journal-title":"Inf Softw Technol"},{"key":"10312_CR7","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/ICSME.2019.00021","volume":"2019","author":"A Barbez","year":"2019","unstructured":"Barbez A, Khomh F, Gueheneuc Y-G (2019) Deep Learning Anti-Patterns from Code Metrics History. IEEE International Conference on Software Maintenance and Evolution (ICSME) 2019:114\u2013124. https:\/\/doi.org\/10.1109\/ICSME.2019.00021","journal-title":"IEEE International Conference on Software Maintenance and Evolution (ICSME)"},{"issue":"8","key":"10312_CR8","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville AC, Vincent P (2013) Representation Learning: A Review and New Perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828. https:\/\/doi.org\/10.1109\/TPAMI.2013.50","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10312_CR9","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","author":"G Brier","year":"1950","unstructured":"Brier G (1950). VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY. https:\/\/doi.org\/10.1175\/1520-0493(1950)078%3c0001:VOFEIT%3e2.0.CO;2","journal-title":"VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY"},{"key":"10312_CR10","unstructured":"Brown WH, Malveau RC, McCormick HWS, Mowbray TJ (1998) AntiPatterns: refactoring software, architectures, and projects in crisis (1st edn). John Wiley & Sons, Inc."},{"key":"10312_CR11","doi-asserted-by":"publisher","unstructured":"Buch, L, Andrzejak, A (2019) Learning-Based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection. 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER), 95\u2013104. https:\/\/doi.org\/10.1109\/SANER.2019.8668039","DOI":"10.1109\/SANER.2019.8668039"},{"key":"10312_CR12","doi-asserted-by":"publisher","unstructured":"Bui, NDQ, Yu, Y, Jiang, L (2021) InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees. 2021 IEEE\/ACM 43rd International Conference on Software Engineering (ICSE), 1186\u20131197. https:\/\/doi.org\/10.1109\/ICSE43902.2021.00109","DOI":"10.1109\/ICSE43902.2021.00109"},{"issue":"02","key":"10312_CR13","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1142\/S021819401950013X","volume":"29","author":"FL Caram","year":"2019","unstructured":"Caram FL, Rodrigues BRDO, Campanelli AS, Parreiras FS (2019) Machine Learning Techniques for Code Smells Detection: A Systematic Mapping Study. Int J Software Eng Knowl Eng 29(02):285\u2013316. https:\/\/doi.org\/10.1142\/S021819401950013X","journal-title":"Int J Software Eng Knowl Eng"},{"key":"10312_CR14","doi-asserted-by":"publisher","unstructured":"Chen, L, Ye, W, Zhang, S (2019) Capturing source code semantics via tree-based convolution over API-enhanced AST. Proceedings of the 16th ACM International Conference on Computing Frontiers, 174\u2013182. https:\/\/doi.org\/10.1145\/3310273.3321560","DOI":"10.1145\/3310273.3321560"},{"key":"10312_CR15","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21:6. https:\/\/doi.org\/10.1186\/s12864-019-6413-7","journal-title":"BMC Genomics"},{"issue":"5","key":"10312_CR16","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1016\/j.infsof.2011.01.004","volume":"53","author":"DS Cruzes","year":"2011","unstructured":"Cruzes DS, Dyb\u00e5 T (2011) Research synthesis in software engineering. Inf Softw Technol 53(5):440\u2013455. https:\/\/doi.org\/10.1016\/j.infsof.2011.01.004","journal-title":"Inf Softw Technol"},{"key":"10312_CR17","doi-asserted-by":"publisher","unstructured":"Das, AK, Yadav, S, Dhal, S (2019) Detecting Code Smells using Deep Learning. TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), 2081\u20132086. https:\/\/doi.org\/10.1109\/TENCON.2019.8929628","DOI":"10.1109\/TENCON.2019.8929628"},{"key":"10312_CR18","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. http:\/\/arxiv.org\/abs\/1810.04805. Accessed 07-03-2022"},{"issue":"1","key":"10312_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17671\/gazibtd.541476","volume":"13","author":"H D\u00ec\u015fl\u00ec","year":"2020","unstructured":"D\u00ec\u015fl\u00ec H, Tosun A (2020) Code Clone Detection with Convolutional Neural Networks. Bili\u015fim Teknolojileri Dergisi 13(1):1\u201312. https:\/\/doi.org\/10.17671\/gazibtd.541476","journal-title":"Bili\u015fim Teknolojileri Dergisi"},{"key":"10312_CR20","doi-asserted-by":"publisher","first-page":"1516","DOI":"10.1109\/IWCMC48107.2020.9148302","volume":"2020","author":"W Dong","year":"2020","unstructured":"Dong W, Feng Z, Wei H, Luo H (2020) A Novel Code Stylometry-based Code Clone Detection Strategy. International Wireless Communications and Mobile Computing (IWCMC) 2020:1516\u20131521. https:\/\/doi.org\/10.1109\/IWCMC48107.2020.9148302","journal-title":"International Wireless Communications and Mobile Computing (IWCMC)"},{"key":"10312_CR21","doi-asserted-by":"publisher","unstructured":"Fakhoury, S, Arnaoudova, V, Noiseux, C, Khomh, F, Antoniol, G (2018) Keep it simple: Is deep learning good for linguistic smell detection? 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER), 602\u2013611. https:\/\/doi.org\/10.1109\/SANER.2018.8330265","DOI":"10.1109\/SANER.2018.8330265"},{"key":"10312_CR22","doi-asserted-by":"publisher","unstructured":"Fang, C, Liu, Z, Shi, Y, Huang, J, Shi, Q (2020) Functional code clone detection with syntax and semantics fusion learning. Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, 516\u2013527. https:\/\/doi.org\/10.1145\/3395363.3397362","DOI":"10.1145\/3395363.3397362"},{"key":"10312_CR23","doi-asserted-by":"publisher","unstructured":"Feng, C, Wang, T, Yu, Y, Zhang, Y, Zhang, Y, Wang, H (2020) Sia-RAE: A Siamese Network based on Recursive AutoEncoder for Effective Clone Detection. 2020 27th Asia-Pacific Software Engineering Conference (APSEC), 238\u2013246. https:\/\/doi.org\/10.1109\/APSEC51365.2020.00032","DOI":"10.1109\/APSEC51365.2020.00032"},{"key":"10312_CR24","doi-asserted-by":"publisher","unstructured":"Fontana FA, M\u00e4ntyl\u00e4 MV, Zanoni M, Marino A\u00a0et al (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","DOI":"10.1007\/s10664-015-9378-4"},{"key":"10312_CR25","unstructured":"Fowler M, Beck K, Brant J, Opdyke W, Roberts D, Gamma E (1999) Refactoring: improving the design of existing code (1 edn). Addison-Wesley Professional"},{"key":"10312_CR26","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1109\/ICSME.2019.00025","volume":"2019","author":"Y Gao","year":"2019","unstructured":"Gao Y, Wang Z, Liu S, Yang L, Sang W, Cai Y (2019) TECCD: A Tree Embedding Approach for Code Clone Detection. IEEE International Conference on Software Maintenance and Evolution (ICSME) 2019:145\u2013156. https:\/\/doi.org\/10.1109\/ICSME.2019.00025","journal-title":"IEEE International Conference on Software Maintenance and Evolution (ICSME)"},{"key":"10312_CR27","doi-asserted-by":"publisher","unstructured":"Gentleman, R, Carey, VJ (2008) Unsupervised Machine Learning. In F. Hahne, W. Huber, R. Gentleman, & S. Falcon (Eds.), Bioconductor Case Studies (pp. 137\u2013157). Springer. https:\/\/doi.org\/10.1007\/978-0-387-77240-0_10","DOI":"10.1007\/978-0-387-77240-0_10"},{"key":"10312_CR28","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press"},{"issue":"3","key":"10312_CR29","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. Software Qual J 28(3):1063\u20131086. https:\/\/doi.org\/10.1007\/s11219-020-09498-y","journal-title":"Software Qual J"},{"key":"10312_CR30","doi-asserted-by":"publisher","unstructured":"Guo, X, Shi, C, Jiang, H (2019) Deep semantic-Based Feature Envy Identification. Proceedings of the 11th Asia-Pacific Symposium on Internetware, 1\u20136. https:\/\/doi.org\/10.1145\/3361242.3361257","DOI":"10.1145\/3361242.3361257"},{"key":"10312_CR31","doi-asserted-by":"publisher","first-page":"24948","DOI":"10.1109\/ACCESS.2020.2966532","volume":"8","author":"C Guo","year":"2020","unstructured":"Guo C, Yang H, Huang D, Zhang J, Dong N, Xu J, Zhu J (2020) Review Sharing via Deep Semi-Supervised Code Clone Detection. IEEE Access 8:24948\u201324965. https:\/\/doi.org\/10.1109\/ACCESS.2020.2966532","journal-title":"IEEE Access"},{"key":"10312_CR32","doi-asserted-by":"publisher","unstructured":"Hadj-Kacem, M, Bouassida, N (2018) A Hybrid Approach To Detect Code Smells using Deep Learning. Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering, 137\u2013146. https:\/\/doi.org\/10.5220\/0006709801370146","DOI":"10.5220\/0006709801370146"},{"key":"10312_CR33","doi-asserted-by":"publisher","unstructured":"Hadj-Kacem, M, Bouassida, N (2019a) Improving the Identification of Code Smells by Combining Structural and Semantic Information. In T. Gedeon, K. W. Wong, & M. Lee (Eds.), Neural Information Processing (pp. 296\u2013304). Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-36808-1_32","DOI":"10.1007\/978-3-030-36808-1_32"},{"key":"10312_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/IJCNN.2019.8851854","volume":"2019","author":"M Hadj-Kacem","year":"2019","unstructured":"Hadj-Kacem M, Bouassida N (2019b) Deep Representation Learning for Code Smells Detection using Variational Auto-Encoder. International Joint Conference on Neural Networks (IJCNN) 2019:1\u20138. https:\/\/doi.org\/10.1109\/IJCNN.2019.8851854","journal-title":"International Joint Conference on Neural Networks (IJCNN)"},{"issue":"6","key":"10312_CR35","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1109\/TSE.2011.103","volume":"38","author":"T Hall","year":"2012","unstructured":"Hall T, Beecham S, Bowes D, Gray D, Counsell S (2012) A Systematic Literature Review on Fault Prediction Performance in Software Engineering. IEEE Trans Software Eng 38(6):1276\u20131304. https:\/\/doi.org\/10.1109\/TSE.2011.103","journal-title":"IEEE Trans Software Eng"},{"key":"10312_CR36","first-page":"2684","volume":"98","author":"A Hamdy","year":"2020","unstructured":"Hamdy A, Tazy M (2020) Deep Hybrid Features for Code Smells Detection. J Theor Appl Inf Technol 98:2684\u20132696","journal-title":"J Theor Appl Inf Technol"},{"issue":"9","key":"10312_CR37","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia EA (2009) Learning from Imbalanced Data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284. https:\/\/doi.org\/10.1109\/TKDE.2008.239","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"10312_CR38","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1109\/TSE.2017.2770124","volume":"45","author":"S Hosseini","year":"2019","unstructured":"Hosseini S, Turhan B, Gunarathna D (2019) A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction. IEEE Trans Software Eng 45(2):111\u2013147. https:\/\/doi.org\/10.1109\/TSE.2017.2770124","journal-title":"IEEE Trans Software Eng"},{"issue":"1","key":"10312_CR39","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, Liu G, Xu G (2021) FCCA: Hybrid Code Representation for Functional Clone Detection Using Attention Networks. IEEE Trans Reliab 70(1):304\u2013318. https:\/\/doi.org\/10.1109\/TR.2020.3001918","journal-title":"IEEE Trans Reliab"},{"issue":"1","key":"10312_CR40","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, Banerjee D, Makedon F (2021) A Survey on Contrastive Self-Supervised Learning. Technologies 9(1):2. https:\/\/doi.org\/10.3390\/technologies9010002","journal-title":"Technologies"},{"issue":"06","key":"10312_CR41","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1142\/S021819402150025X","volume":"31","author":"X Ji","year":"2021","unstructured":"Ji X, Liu L, Zhu J (2021) Code Clone Detection with Hierarchical Attentive Graph Embedding. Int J Software Eng Knowl Eng 31(06):837\u2013861. https:\/\/doi.org\/10.1142\/S021819402150025X","journal-title":"Int J Software Eng Knowl Eng"},{"key":"10312_CR42","doi-asserted-by":"publisher","unstructured":"Jiang, L, Misherghi, G, Su, Z, Glondu, S (2007) DECKARD: Scalable and Accurate Tree-Based Detection of Code Clones. 29th International Conference on Software Engineering (ICSE\u201907), 96\u2013105. https:\/\/doi.org\/10.1109\/ICSE.2007.30","DOI":"10.1109\/ICSE.2007.30"},{"issue":"14","key":"10312_CR43","doi-asserted-by":"publisher","first-page":"6613","DOI":"10.3390\/app11146613","volume":"11","author":"Y-B Jo","year":"2021","unstructured":"Jo Y-B, Lee J, Yoo C-J (2021) Two-Pass Technique for Clone Detection and Type Classification Using Tree-Based Convolution Neural Network. Appl Sci 11(14):6613. https:\/\/doi.org\/10.3390\/app11146613","journal-title":"Appl Sci"},{"key":"10312_CR44","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.protcy.2012.02.068","volume":"1","author":"EM Karabulut","year":"2012","unstructured":"Karabulut EM, \u00d6zel SA, \u0130brik\u00e7i T (2012) A comparative study on the effect of feature selection on classification accuracy. Procedia Technol 1:323\u2013327. https:\/\/doi.org\/10.1016\/j.protcy.2012.02.068","journal-title":"Procedia Technol"},{"key":"10312_CR45","doi-asserted-by":"publisher","unstructured":"Kaur, A, Jain, S, Goel, S, Dhiman, G (2020) A Review on Machine-learning Based Code Smell Detection Techniques in Object-oriented Software System(s). https:\/\/doi.org\/10.2174\/2352096513999200922125839","DOI":"10.2174\/2352096513999200922125839"},{"issue":"3","key":"10312_CR46","doi-asserted-by":"publisher","first-page":"17","DOI":"10.4018\/IJOSSP.2021070102","volume":"12","author":"A Kaur","year":"2021","unstructured":"Kaur A, Saini M (2021) Enhancing the Software Clone Detection in BigCloneBench: A Neural Network Approach. International Journal of Open Source Software and Processes (IJOSSP) 12(3):17\u201331. https:\/\/doi.org\/10.4018\/IJOSSP.2021070102","journal-title":"International Journal of Open Source Software and Processes (IJOSSP)"},{"key":"10312_CR47","unstructured":"Khan MA, Le H, Do K, Tran T, Ghose A, Dam K, Sindhgatta R (2018) Memory-augmented neural networks for predictive process analytics. arXiv preprint. https:\/\/arxiv.org\/abs\/1802.00938. Accessed 07-01-2022"},{"issue":"5","key":"10312_CR48","doi-asserted-by":"publisher","first-page":"3804","DOI":"10.11591\/ijece.v9i5.pp3804-3812","volume":"9","author":"DK Kim","year":"2019","unstructured":"Kim DK (2019) Enhancing code clone detection using control flow graphs. Int J Electric Comput Eng (IJECE) 9(5):3804. https:\/\/doi.org\/10.11591\/ijece.v9i5.pp3804-3812","journal-title":"Int J Electric Comput Eng (IJECE)"},{"key":"10312_CR49","doi-asserted-by":"publisher","unstructured":"Kim DK (2020) A Deep Neural Network-Based Approach to Finding Similar Code Segments. IEICE Trans Inf Syst E103D(4):874\u2013878. https:\/\/doi.org\/10.1587\/transinf.2019EDL8195","DOI":"10.1587\/transinf.2019EDL8195"},{"key":"10312_CR50","unstructured":"Kitchenham B (2004) Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1\u201326."},{"key":"10312_CR51","unstructured":"Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report."},{"issue":"1","key":"10312_CR52","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.infsof.2008.09.009","volume":"51","author":"B Kitchenham","year":"2009","unstructured":"Kitchenham B, Pearl Brereton O, Budgen D, Turner M, Bailey J, Linkman S (2009) Systematic literature reviews in software engineering \u2013 A systematic literature review. Inf Softw Technol 51(1):7\u201315. https:\/\/doi.org\/10.1016\/j.infsof.2008.09.009","journal-title":"Inf Softw Technol"},{"key":"10312_CR53","unstructured":"Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249\u2013268"},{"key":"10312_CR54","doi-asserted-by":"publisher","first-page":"110610","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":"10312_CR55","unstructured":"Le QV, Ngiam J, Coates A, Lahiri A, Prochnow B, Ng AY (2011) On optimization methods for deep learning. In Proceedings of the 28th International Conference on International Conference on Machine Learning (ICML'11). Omnipress, Madison, WI, USA, pp 265\u2013272"},{"key":"10312_CR56","doi-asserted-by":"publisher","unstructured":"Lei, M, Li, H, Li, J, Aundhkar, N, Kim, D-K (2022) Deep learning application on code clone detection: A review of current knowledge. J Syst Softw, 184(C). https:\/\/doi.org\/10.1016\/j.jss.2021.111141","DOI":"10.1016\/j.jss.2021.111141"},{"key":"10312_CR57","doi-asserted-by":"publisher","unstructured":"Lewowski, T, Madeyski, L (2022) Code Smells Detection Using Artificial Intelligence Techniques: A\u00a0Business-Driven Systematic Review. In N. Kryvinska & A. Poniszewska-Mara\u0144da (Eds.), Developments in Information & Knowledge Management for Business Applications: Volume 3 (pp. 285\u2013319). Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-77916-0_12","DOI":"10.1007\/978-3-030-77916-0_12"},{"key":"10312_CR58","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1109\/ICSME.2017.46","volume":"2017","author":"L Li","year":"2017","unstructured":"Li L, Feng H, Zhuang W, Meng N, Ryder B (2017a) CCLearner: A Deep Learning-Based Clone Detection Approach. IEEE International Conference on Software Maintenance and Evolution (ICSME) 2017:249\u2013260. https:\/\/doi.org\/10.1109\/ICSME.2017.46","journal-title":"IEEE International Conference on Software Maintenance and Evolution (ICSME)"},{"key":"10312_CR59","doi-asserted-by":"publisher","unstructured":"Li, Y, Tarlow, D, Brockschmidt, M, Zemel, R (2017b) Gated Graph Sequence Neural Networks (arXiv:1511.05493). arXiv. https:\/\/doi.org\/10.48550\/arXiv.1511.05493","DOI":"10.48550\/arXiv.1511.05493"},{"key":"10312_CR60","doi-asserted-by":"publisher","unstructured":"Li, B, Ye, C, Guan, S, Zhou, H (2020a) Semantic Code Clone Detection Via Event Embedding Tree and GAT Network. 2020a IEEE 20th International Conference on Software Quality, Reliability and Security (QRS), 382\u2013393. https:\/\/doi.org\/10.1109\/QRS51102.2020.00057","DOI":"10.1109\/QRS51102.2020.00057"},{"key":"10312_CR61","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1109\/ICHCI51889.2020.00078","volume":"2020","author":"G Li","year":"2020","unstructured":"Li G, Tang Y, Zhang X, Yi B (2020b) A Deep Learning Based Approach to Detect Code Clones. International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI) 2020:337\u2013340. https:\/\/doi.org\/10.1109\/ICHCI51889.2020.00078","journal-title":"International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI)"},{"key":"10312_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/IJCNN52387.2021.9534099","volume":"2021","author":"H Liang","year":"2021","unstructured":"Liang H, Ai L (2021) AST-path Based Compare-Aggregate Network for Code Clone Detection. International Joint Conference on Neural Networks (IJCNN) 2021:1\u20138. https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9534099","journal-title":"International Joint Conference on Neural Networks (IJCNN)"},{"issue":"3","key":"10312_CR63","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(3):203\u2013228. https:\/\/doi.org\/10.1023\/A:1007608224229","journal-title":"Mach Learn"},{"key":"10312_CR64","doi-asserted-by":"publisher","unstructured":"Liu, H, Jin, J, Xu, Z, Bu, Y, Zou, Y, Zhang, L (2019) Deep Learning Based Code Smell Detection. IEEE Trans Soft Eng, 1\u20131. https:\/\/doi.org\/10.1109\/TSE.2019.2936376","DOI":"10.1109\/TSE.2019.2936376"},{"key":"10312_CR65","doi-asserted-by":"crossref","unstructured":"Liu, X, Zhang, F, Hou, Z, Wang, Z, Mian, L, Zhang, J, Tang, J (2021) Self-supervised Learning: Generative or Contrastive. ArXiv:2006.08218 [Cs, Stat]. http:\/\/arxiv.org\/abs\/2006.08218","DOI":"10.1109\/TKDE.2021.3090866"},{"key":"10312_CR66","unstructured":"Ma Y, He H (eds) (2013) Imbalanced learning: foundations, algorithms, and applications (1st edn). Wiley-IEEE Press."},{"key":"10312_CR67","unstructured":"Marinescu C, Marinescu R, Mihancea PF, Ratiu D, Wettel R (2005) Iplasma: an integrated platform for quality assessment of object-oriented design. ICSM, pp 77\u201380"},{"issue":"8","key":"10312_CR68","doi-asserted-by":"publisher","first-page":"e2255","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(8):e2255. https:\/\/doi.org\/10.1002\/smr.2255","journal-title":"J Softw Evol Process"},{"key":"10312_CR69","doi-asserted-by":"publisher","unstructured":"Mehrotra, N, Agarwal, N, Gupta, P, Anand, S, Lo, D, Purandare, R (2021) Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks. IEEE Trans Softw Eng, 1\u20131. https:\/\/doi.org\/10.1109\/TSE.2021.3105556","DOI":"10.1109\/TSE.2021.3105556"},{"key":"10312_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/5027198","volume":"2020","author":"Y Meng","year":"2020","unstructured":"Meng Y, Liu L (2020) A Deep Learning Approach for a Source Code Detection Model Using Self-Attention. Complexity 2020:1\u201315. https:\/\/doi.org\/10.1155\/2020\/5027198","journal-title":"Complexity"},{"key":"10312_CR71","doi-asserted-by":"publisher","unstructured":"Menshawy, RS, Yousef, AH, Salem, A (2021) Code Smells and Detection Techniques: A Survey. 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), 78\u201383. https:\/\/doi.org\/10.1109\/MIUCC52538.2021.9447669","DOI":"10.1109\/MIUCC52538.2021.9447669"},{"issue":"1","key":"10312_CR72","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(1):20\u201336. https:\/\/doi.org\/10.1109\/TSE.2009.50","journal-title":"IEEE Trans Softw Eng"},{"key":"10312_CR73","doi-asserted-by":"publisher","first-page":"110686","DOI":"10.1016\/j.jss.2020.110686","volume":"169","author":"G Mostaeen","year":"2020","unstructured":"Mostaeen G, Roy B, Roy CK, Schneider K, Svajlenko J (2020) A machine learning based framework for code clone validation. J Syst Softw 169:110686. https:\/\/doi.org\/10.1016\/j.jss.2020.110686","journal-title":"J Syst Softw"},{"issue":"3","key":"10312_CR74","doi-asserted-by":"publisher","first-page":"e2154","DOI":"10.1002\/smr.2154","volume":"31","author":"H Mumtaz","year":"2019","unstructured":"Mumtaz H, Alshayeb M, Mahmood S, Niazi M (2019) A survey on UML model smells detection techniques for software refactoring. J Softw Evol Process 31(3):e2154. https:\/\/doi.org\/10.1002\/smr.2154","journal-title":"J Softw Evol Process"},{"key":"10312_CR75","doi-asserted-by":"publisher","first-page":"110491","DOI":"10.1016\/j.jss.2019.110491","volume":"162","author":"KW Nafi","year":"2020","unstructured":"Nafi KW, Roy B, Roy CK, Schneider KA (2020) A universal cross language software similarity detector for open source software categorization. J Syst Softw 162:110491. https:\/\/doi.org\/10.1016\/j.jss.2019.110491","journal-title":"J Syst Softw"},{"key":"10312_CR76","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. 2019 34th IEEE\/ACM International Conference on Automated Software Engineering (ASE), 1026\u20131037. https:\/\/doi.org\/10.1109\/ASE.2019.00099","DOI":"10.1109\/ASE.2019.00099"},{"key":"10312_CR77","doi-asserted-by":"publisher","unstructured":"Nair, A, Roy, A, Meinke, K (2020) funcGNN: A Graph Neural Network Approach to Program Similarity. Proceedings of the 14th ACM \/ IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 1\u201311. https:\/\/doi.org\/10.1145\/3382494.3410675","DOI":"10.1145\/3382494.3410675"},{"key":"10312_CR78","doi-asserted-by":"publisher","first-page":"107090","DOI":"10.1016\/j.knosys.2021.107090","volume":"224","author":"K Ohri","year":"2021","unstructured":"Ohri K, Kumar M (2021) Review on self-supervised image recognition using deep neural networks. Knowl Based Syst 224:107090. https:\/\/doi.org\/10.1016\/j.knosys.2021.107090","journal-title":"Knowl Based Syst"},{"key":"10312_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICSM.2010.5609564","volume":"2010","author":"SM Olbrich","year":"2010","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. IEEE International Conference on Software Maintenance 2010:1\u201310. https:\/\/doi.org\/10.1109\/ICSM.2010.5609564","journal-title":"IEEE International Conference on Software Maintenance"},{"key":"10312_CR80","doi-asserted-by":"publisher","unstructured":"Palomba, F, Di Nucci, D, Tufano, M, Bavota, G, Oliveto, R, Poshyvanyk, D, De Lucia, A (2015) Landfill: An Open Dataset of Code Smells with Public Evaluation. 2015 IEEE\/ACM 12th Working Conference on Mining Software Repositories, 482\u2013485. https:\/\/doi.org\/10.1109\/MSR.2015.69","DOI":"10.1109\/MSR.2015.69"},{"issue":"2","key":"10312_CR81","doi-asserted-by":"publisher","first-page":"21","DOI":"10.4018\/IJOSSP.2021040102","volume":"12","author":"A Patnaik","year":"2021","unstructured":"Patnaik A, Padhy N (2021) A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms. International Journal of Open Source Software and Processes 12(2):21\u201335. https:\/\/doi.org\/10.4018\/IJOSSP.2021040102","journal-title":"International Journal of Open Source Software and Processes"},{"key":"10312_CR82","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2020.110693","author":"F Pecorelli","year":"2020","unstructured":"Pecorelli F, Nucci DD, Roover CD, Lucia AD (2020) A large empirical assessment of the role of data balancing in machine-learning-based code smell detection. J Syst Softw. https:\/\/doi.org\/10.1016\/j.jss.2020.110693","journal-title":"J Syst Softw"},{"key":"10312_CR83","doi-asserted-by":"publisher","unstructured":"Perez, D, Chiba, S (2019) Cross-Language Clone Detection by Learning Over Abstract Syntax Trees. 2019 IEEE\/ACM 16th International Conference on Mining Software Repositories (MSR), 518\u2013528. https:\/\/doi.org\/10.1109\/MSR.2019.00078","DOI":"10.1109\/MSR.2019.00078"},{"key":"10312_CR84","doi-asserted-by":"publisher","unstructured":"P\u00e9rez, J (2013) Refactoring Planning for Design Smell Correction: Summary, Opportunities and Lessons Learned. Proceedings of the 2013 IEEE International Conference on Software Maintenance, 572\u2013577. https:\/\/doi.org\/10.1109\/ICSM.2013.98","DOI":"10.1109\/ICSM.2013.98"},{"key":"10312_CR85","first-page":"294","volume":"13","author":"CE Rasmussen","year":"2001","unstructured":"Rasmussen CE, Ghahramani Z (2001) Occam\u2019s Razor. In Advances in Neural Information Processing Systems 13:294\u2013300","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"10312_CR86","doi-asserted-by":"publisher","unstructured":"Ren, S, Shi, C, Zhao, S (2021) Exploiting Multi-aspect Interactions for God Class Detection with Dataset Fine-tuning. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), 864\u2013873. https:\/\/doi.org\/10.1109\/COMPSAC51774.2021.00119","DOI":"10.1109\/COMPSAC51774.2021.00119"},{"issue":"1","key":"10312_CR87","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/spe.2639","volume":"49","author":"F Sabir","year":"2019","unstructured":"Sabir F, Palma F, Rasool G, Gu\u00e9h\u00e9neuc Y-G, Moha N (2019) A systematic literature review on the detection of smells and their evolution in object-oriented and service-oriented systems. Softw Practice Experience 49(1):3\u201339. https:\/\/doi.org\/10.1002\/spe.2639","journal-title":"Softw Practice Experience"},{"key":"10312_CR88","doi-asserted-by":"publisher","unstructured":"Saini, V, Farmahinifarahani, F, Lu, Y, Baldi, P, Lopes, CV (2018) Oreo: Detection of clones in the twilight zone. Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 354\u2013365. https:\/\/doi.org\/10.1145\/3236024.3236026","DOI":"10.1145\/3236024.3236026"},{"key":"10312_CR89","doi-asserted-by":"publisher","unstructured":"Saini, V, Farmahinifarahani, F, Lu, Y, Yang, D, Martins, P, Sajnani, H, Baldi, P, Lopes, CV (2019) Towards Automating Precision Studies of Clone Detectors. 2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE), 49\u201359. https:\/\/doi.org\/10.1109\/ICSE.2019.00023","DOI":"10.1109\/ICSE.2019.00023"},{"key":"10312_CR90","doi-asserted-by":"publisher","unstructured":"Sajnani, H, Saini, V, Svajlenko, J, Roy, C K, Lopes, CV (2016) SourcererCC: Scaling Code Clone Detection to Big-Code. 2016 IEEE\/ACM 38th International Conference on Software Engineering (ICSE), 1157\u20131168. https:\/\/doi.org\/10.1145\/2884781.2884877","DOI":"10.1145\/2884781.2884877"},{"key":"10312_CR91","doi-asserted-by":"publisher","unstructured":"Sammut C, Webb GI (2011) Encyclopedia of machine learning. Springer Sci Bus Med. https:\/\/doi.org\/10.1007\/978-0-387-30164-8","DOI":"10.1007\/978-0-387-30164-8"},{"issue":"6","key":"10312_CR92","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker IH (2021) Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science 2(6):420. https:\/\/doi.org\/10.1007\/s42979-021-00815-1","journal-title":"SN Computer Science"},{"key":"10312_CR93","doi-asserted-by":"publisher","first-page":"110936","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":"10312_CR94","doi-asserted-by":"publisher","first-page":"84828","DOI":"10.1109\/ACCESS.2021.3079156","volume":"9","author":"A Sheneamer","year":"2021","unstructured":"Sheneamer A, Roy S, Kalita J (2021) An Effective Semantic Code Clone Detection Framework Using Pairwise Feature Fusion. IEEE Access 9:84828\u201384844. https:\/\/doi.org\/10.1109\/ACCESS.2021.3079156","journal-title":"IEEE Access"},{"key":"10312_CR95","doi-asserted-by":"publisher","unstructured":"Sheneamer, A, Hazazi, H, Roy, S, Kalita, J (2017) Schemes for Labeling Semantic Code Clones using Machine Learning. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 981\u2013985. https:\/\/doi.org\/10.1109\/ICMLA.2017.00-25","DOI":"10.1109\/ICMLA.2017.00-25"},{"key":"10312_CR96","doi-asserted-by":"publisher","unstructured":"Sheneamer, A (2018) CCDLC Detection Framework-Combining Clustering with Deep Learning Classification for Semantic Clones. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 701\u2013706. https:\/\/doi.org\/10.1109\/ICMLA.2018.00111","DOI":"10.1109\/ICMLA.2018.00111"},{"key":"10312_CR97","doi-asserted-by":"publisher","unstructured":"Sidhu, BK, Singh, K, Sharma, N (2020) A machine learning approach to software model refactoring. Int J Comput Appl, 1\u201312. https:\/\/doi.org\/10.1080\/1206212X.2020.1711616","DOI":"10.1080\/1206212X.2020.1711616"},{"key":"10312_CR98","doi-asserted-by":"publisher","unstructured":"Storey, M-A, Zagalsky, A (2016) Disrupting developer productivity one bot at a time. Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, 928\u2013931. https:\/\/doi.org\/10.1145\/2950290.2983989","DOI":"10.1145\/2950290.2983989"},{"key":"10312_CR99","doi-asserted-by":"publisher","unstructured":"Suryanarayana, G, Samarthyam, G, Sharma, T (2015) Refactoring for Software Design Smells: Managing Technical Debt, Chapter 2\u2014Design Smells. In G. Suryanarayana, G. Samarthyam, & T. Sharma (Eds.), Refactoring for Software Design Smells (pp. 9\u201319). Morgan Kaufmann. https:\/\/doi.org\/10.1016\/B978-0-12-801397-7.00002-3","DOI":"10.1016\/B978-0-12-801397-7.00002-3"},{"key":"10312_CR100","unstructured":"Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. Proceedings of the 30th International Conference on Machine Learning, 1139\u20131147. https:\/\/proceedings.mlr.press\/v28\/sutskever13.html. Accessed 01 Jan 2022"},{"key":"10312_CR101","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1109\/ICSME.2014.77","volume":"2014","author":"J Svajlenko","year":"2014","unstructured":"Svajlenko J, Islam JF, Keivanloo I, Roy CK, Mia MM (2014) Towards a Big Data Curated Benchmark of Inter-project Code Clones. IEEE International Conference on Software Maintenance and Evolution 2014:476\u2013480. https:\/\/doi.org\/10.1109\/ICSME.2014.77","journal-title":"IEEE International Conference on Software Maintenance and Evolution"},{"issue":"7","key":"10312_CR102","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 Software Eng 45(7):683\u2013711. https:\/\/doi.org\/10.1109\/TSE.2018.2794977","journal-title":"IEEE Trans Software Eng"},{"key":"10312_CR103","doi-asserted-by":"publisher","unstructured":"Tsantalis, N, Chaikalis, T, Chatzigeorgiou, A (2008) JDeodorant: Identification and Removal of Type-Checking Bad Smells. 2008 12th European Conference on Software Maintenance and Reengineering, 329\u2013331. https:\/\/doi.org\/10.1109\/CSMR.2008.4493342","DOI":"10.1109\/CSMR.2008.4493342"},{"key":"10312_CR104","doi-asserted-by":"publisher","unstructured":"Tufano, M, Watson, C, Bavota, G, Di Penta, M, White, M, Poshyvanyk, D (2018) Deep learning similarities from different representations of source code. Proceedings of the 15th International Conference on Mining Software Repositories, 542\u2013553. https:\/\/doi.org\/10.1145\/3196398.3196431","DOI":"10.1145\/3196398.3196431"},{"issue":"11","key":"10312_CR105","doi-asserted-by":"publisher","first-page":"3115","DOI":"10.1007\/s13042-020-01246-9","volume":"12","author":"F Ullah","year":"2021","unstructured":"Ullah F, Naeem MR, Mostarda L, Shah SA (2021) Clone detection in 5G-enabled social IoT system using graph semantics and deep learning model. Int J Mach Learn Cybern 12(11):3115\u20133127. https:\/\/doi.org\/10.1007\/s13042-020-01246-9","journal-title":"Int J Mach Learn Cybern"},{"issue":"4","key":"10312_CR106","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3409331","volume":"29","author":"W Wang","year":"2020","unstructured":"Wang W, Li G, Shen S, Xia X, Jin Z (2020c) Modular Tree Network for Source Code Representation Learning. ACM Transactions on Software Engineering and Methodology 29(4):1\u201323. https:\/\/doi.org\/10.1145\/3409331","journal-title":"ACM Transactions on Software Engineering and Methodology"},{"key":"10312_CR107","doi-asserted-by":"publisher","unstructured":"Wang, C, Gao, J, Jiang, Y, Xing, Z, Zhang, H, Yin, W, Gu, M, Sun, J (2019) Go-clone: Graph-embedding based clone detector for Golang. Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis, 374\u2013377. https:\/\/doi.org\/10.1145\/3293882.3338996","DOI":"10.1145\/3293882.3338996"},{"key":"10312_CR108","doi-asserted-by":"publisher","unstructured":"Wang, H, Liu, J, Kang, J, Yin, W, Sun, H, Wang, H (2020a). Feature Envy Detection based on Bi-LSTM with Self-Attention Mechanism. 2020a IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA\/BDCloud\/SocialCom\/SustainCom), 448\u2013457. https:\/\/doi.org\/10.1109\/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00082","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00082"},{"key":"10312_CR109","doi-asserted-by":"publisher","unstructured":"Wang, W, Li, G, Ma, B, Xia, X, Jin, Z (2020b) Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree. 2020b IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER), 261\u2013271. https:\/\/doi.org\/10.1109\/SANER48275.2020.9054857","DOI":"10.1109\/SANER48275.2020.9054857"},{"key":"10312_CR110","doi-asserted-by":"publisher","unstructured":"Wei, H, Li, M (2017) Supervised Deep Features for Software Functional Clone Detection by Exploiting Lexical and Syntactical Information in Source Code. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 3034\u20133040. https:\/\/doi.org\/10.24963\/ijcai.2017\/423","DOI":"10.24963\/ijcai.2017\/423"},{"key":"10312_CR111","doi-asserted-by":"publisher","unstructured":"Wei, H-H, Li, M (2018) Positive and Unlabeled Learning for Detecting Software Functional Clones with Adversarial Training. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2840\u20132846. https:\/\/doi.org\/10.24963\/ijcai.2018\/394","DOI":"10.24963\/ijcai.2018\/394"},{"issue":"1","key":"10312_CR112","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.infsof.2011.09.002","volume":"54","author":"J Wen","year":"2012","unstructured":"Wen J, Li S, Lin Z, Hu Y, Huang C (2012) Systematic literature review of machine learning based software development effort estimation models. Inf Softw Technol 54(1):41\u201359. https:\/\/doi.org\/10.1016\/j.infsof.2011.09.002","journal-title":"Inf Softw Technol"},{"key":"10312_CR113","doi-asserted-by":"publisher","unstructured":"White M, Tufano M, Vendome C, Poshyvanyk D (2016) Deep learning code fragments for code clone detection. 2016 31st IEEE\/ACM International Conference on Automated Software Engineering (ASE), 87\u201398. https:\/\/doi.org\/10.1145\/2970276.2970326","DOI":"10.1145\/2970276.2970326"},{"key":"10312_CR114","doi-asserted-by":"publisher","unstructured":"Wu, Y, Zou, D, Dou, S, Yang, S, Yang, W, Cheng, F, Liang, H, Jin, H (2020) SCDetector: Software functional clone detection based on semantic tokens analysis. Proceedings of the 35th IEEE\/ACM International Conference on Automated Software Engineering, 821\u2013833. https:\/\/doi.org\/10.1145\/3324884.3416562","DOI":"10.1145\/3324884.3416562"},{"key":"10312_CR115","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/ICICSE52190.2021.9404110","volume":"2021","author":"Y Wu","year":"2021","unstructured":"Wu Y, Wang W (2021) Code Similarity Detection Based on Siamese Network. IEEE International Conference on Information Communication and Software Engineering (ICICSE) 2021:47\u201351. https:\/\/doi.org\/10.1109\/ICICSE52190.2021.9404110","journal-title":"IEEE International Conference on Information Communication and Software Engineering (ICICSE)"},{"issue":"21","key":"10312_CR116","doi-asserted-by":"publisher","first-page":"7519","DOI":"10.3390\/app10217519","volume":"10","author":"C Xie","year":"2020","unstructured":"Xie C, Wang X, Qian C, Wang M (2020) A Source Code Similarity Based on Siamese Neural Network. Appl Sci 10(21):7519. https:\/\/doi.org\/10.3390\/app10217519","journal-title":"Appl Sci"},{"key":"10312_CR117","doi-asserted-by":"publisher","unstructured":"Xu, W (2021) Multi-Granularity Code Smell Detection using Deep Learning Method based on Abstract Syntax Tree. 503\u2013509. https:\/\/doi.org\/10.18293\/SEKE2021-014","DOI":"10.18293\/SEKE2021-014"},{"key":"10312_CR118","doi-asserted-by":"publisher","unstructured":"Xue, H, Venkataramani, G, Lan, T (2018) Clone-Slicer: Detecting Domain Specific Binary Code Clones through Program Slicing. Proceedings of the 2018 Workshop on Forming an Ecosystem Around Software Transformation - FEAST \u201918, 27\u201333. https:\/\/doi.org\/10.1145\/3273045.3273047","DOI":"10.1145\/3273045.3273047"},{"issue":"86","key":"10312_CR119","doi-asserted-by":"publisher","first-page":"2639","DOI":"10.1016\/j.jss.2013.05.007","volume":"10","author":"A Yamashita","year":"2013","unstructured":"Yamashita A, Counsell S (2013) Code smells as system-level indicators of maintainability: An empirical study. J Syst Softw 10(86):2639\u20132653. https:\/\/doi.org\/10.1016\/j.jss.2013.05.007","journal-title":"J Syst Softw"},{"key":"10312_CR120","doi-asserted-by":"publisher","unstructured":"Yin, X, Shi, C, Zhao, S (2021) Local and Global Feature Based Explainable Feature Envy Detection. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), 942\u2013951. https:\/\/doi.org\/10.1109\/COMPSAC51774.2021.00127","DOI":"10.1109\/COMPSAC51774.2021.00127"},{"key":"10312_CR121","doi-asserted-by":"publisher","unstructured":"Yu, H, Lam, W, Chen, L, Li, G, Xie, T, Wang, Q (2019) Neural Detection of Semantic Code Clones Via Tree-Based Convolution. 2019 IEEE\/ACM 27th International Conference on Program Comprehension (ICPC), 70\u201380. https:\/\/doi.org\/10.1109\/ICPC.2019.00021","DOI":"10.1109\/ICPC.2019.00021"},{"key":"10312_CR122","doi-asserted-by":"publisher","unstructured":"Yuan, Y, Kong, W, Hou, G, Hu, Y, Watanabe, M, Fukuda, A (2020) From Local to Global Semantic Clone Detection. 2019 6th International Conference on Dependable Systems and Their Applications (DSA), 13\u201324. https:\/\/doi.org\/10.1109\/DSA.2019.00012","DOI":"10.1109\/DSA.2019.00012"},{"key":"10312_CR123","doi-asserted-by":"publisher","first-page":"125062","DOI":"10.1109\/ACCESS.2019.2938825","volume":"7","author":"J Zeng","year":"2019","unstructured":"Zeng J, Ben K, Li X, Zhang X (2019) Fast Code Clone Detection Based on Weighted Recursive Autoencoders. IEEE Access 7:125062\u2013125078. https:\/\/doi.org\/10.1109\/ACCESS.2019.2938825","journal-title":"IEEE Access"},{"key":"10312_CR124","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/ICICSE52190.2021.9404141","volume":"2021","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Wang T (2021) CCEyes: An Effective Tool for Code Clone Detection on Large-Scale Open Source Repositories. IEEE International Conference on Information Communication and Software Engineering (ICICSE) 2021:61\u201370. https:\/\/doi.org\/10.1109\/ICICSE52190.2021.9404141","journal-title":"IEEE International Conference on Information Communication and Software Engineering (ICICSE)"},{"issue":"3","key":"10312_CR125","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1002\/smr.521","volume":"23","author":"M Zhang","year":"2011","unstructured":"Zhang M, Hall T, Baddoo N (2011) Code Bad Smells: A review of current knowledge. J Softw Maint Evol Res Pract 23(3):179\u2013202. https:\/\/doi.org\/10.1002\/smr.521","journal-title":"J Softw Maint Evol Res Pract"},{"key":"10312_CR126","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.1109\/IWCMC48107.2020.9148382","volume":"2020","author":"L Zhang","year":"2020","unstructured":"Zhang L, Feng Z, Ren W, Luo H (2020) Siamese-Based BiLSTM Network for Scratch Source Code Similarity Measuring. International Wireless Communications and Mobile Computing (IWCMC) 2020:1800\u20131805. https:\/\/doi.org\/10.1109\/IWCMC48107.2020.9148382","journal-title":"International Wireless Communications and Mobile Computing (IWCMC)"},{"key":"10312_CR127","doi-asserted-by":"publisher","unstructured":"Zhang, J, Wang, X, Zhang, H, Sun, H, Wang, K, Liu, X (2019) A Novel Neural Source Code Representation Based on Abstract Syntax Tree. 2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE), 783\u2013794. https:\/\/doi.org\/10.1109\/ICSE.2019.00086","DOI":"10.1109\/ICSE.2019.00086"},{"key":"10312_CR128","doi-asserted-by":"publisher","unstructured":"Zhang, J, Hong, H, Zhang, Y, Wan, Y, Liu, Y, Sui, Y (2021) Disentangled Code Representation Learning for Multiple Programming Languages. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 4454\u20134466. https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.391","DOI":"10.18653\/v1\/2021.findings-acl.391"},{"key":"10312_CR129","doi-asserted-by":"publisher","unstructured":"Zhao, G, Huang, J (2018) DeepSim: Deep learning code functional similarity. Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 141\u2013151. https:\/\/doi.org\/10.1145\/3236024.3236068","DOI":"10.1145\/3236024.3236068"},{"key":"10312_CR130","doi-asserted-by":"publisher","unstructured":"Zhou, X, Jin, Y, Zhang, H, Li, S, Huang, X (2016) A Map of Threats to Validity of Systematic Literature Reviews in Software Engineering. 2016 23rd Asia-Pacific Software Engineering Conference (APSEC), 153\u2013160. https:\/\/doi.org\/10.1109\/APSEC.2016.031","DOI":"10.1109\/APSEC.2016.031"}],"container-title":["Empirical Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-023-10312-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10664-023-10312-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-023-10312-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T21:04:25Z","timestamp":1685135065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10664-023-10312-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5]]},"references-count":130,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["10312"],"URL":"https:\/\/doi.org\/10.1007\/s10664-023-10312-z","relation":{},"ISSN":["1382-3256","1573-7616"],"issn-type":[{"value":"1382-3256","type":"print"},{"value":"1573-7616","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5]]},"assertion":[{"value":"1 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2023","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 have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interests\/Competing Interests"}}],"article-number":"77"}}