[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127825-en":3,"doc-seo-127825-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127825,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning-Based Methods for Code Smell Detection - A Survey","Code smells act as early warnings for potential software quality problems, and many detection techniques have been proposed, including probabilistic, rule-based, and antipattern-focused methods as well as machine-learning strategies. This systematic review consolidates studies from 2005 to 2024 that use machine learning algorithms, examining commonly used ML approaches, software metrics, and dataset characteristics. Forty-two studies across open-source and project datasets are analyzed to evaluate effectiveness and identify practical barriers such as nonstandard definitions, feature-selection difficulty, and large-scale data handling challenges.","applied sciences  \nSystematic Review  \nMachine Learning-Based Methods for Code Smell Detection: A Survey  \nPravin Singh Yadav 1, Rajwant Singh Rao 1, Alok Mishra 2,3, * and Manjari Gupta 4  \nCitation: Yadav, P.S.; Rao, R.S.; Mishra, A.; Gupta, M. Machine Learning-Based Methods for Code Smell Detection: A Survey. Appl. Sci.  \n2024, 14, 6149. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/app14146149](10.3390/app14146149)  \nAcademic Editors: Andrea Prati, Linda Vickovic and Maja Braovi´c  \nReceived: 20 April 2024  \nRevised: 20 June 2024  \nAccepted: 11 July 2024  \nPublished: 15 July 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur 495009, Chhattisgarh, India; [pravinsingh1110@gmail.com](pravinsingh1110@gmail.com) (P.S.Y.); [rajwantrao@gmail.com](rajwantrao@gmail.com) (R.S.R.)  \n2 Faculty of Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway  \n3 Informatics and Digitalization Group, Molde University College, Specialized University in Logistics, 6402 Molde, Norway  \n4 Computer Science, DST—Centre for Interdisciplinary Mathematical Sciences, Institute of Science, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India; [manjari@bhu.ac.in](manjari@bhu.ac.in)  \n* Correspondence: [alok.mishra@ntnu.no](alok.mishra@ntnu.no)  \nAbstract: Code smells are early warning signs of potential issues in software quality. Various techniques are used in code smell detection, including the Bayesian approach, rule-based automatic antipattern detection, antipattern identification utilizing B-splines, Support Vector Machine direct, SMURF (Support Vector Machines for design smell detection using relevant feedback), and immunebased detection strategy. Machine learning (ML) has taken a great stride in this area. This study includes relevant studies applying ML algorithms from 2005 to 2024 in a comprehensive manner for the survey to provide insight regarding code smell, ML algorithms frequently applied, and software metrics. Forty-two pertinent studies allow us to assess the efficacy of ML algorithms on selected datasets. After evaluating various studies based on open-source and project datasets, this study evaluated additional threats and obstacles to code smell detection, such as the lack of standardized code smell definitions, the difficulty of feature selection, and the challenges of handling large-scale datasets. The current studies only considered a few factors in identifying code smells, while in this study, several potential contributing factors to code smells are included. Several ML algorithms are examined, and various approaches, datasets, dataset languages, and software metrics are presented. This study provides the potential of ML algorithms to produce better results and fills a gap in the body of knowledge by providing class-wise distributions of the ML algorithms. Support Vector Machine, J48, Naive Bayes, and Random Forest models are the most common for detecting code smells. Researchers can find this study helpful in better anticipating and taking care of software development design and implementation issues. The findings from this study, which highlight the practical implications of ML algorithms in software quality improvement, will help software engineers fix problems during software design and development to ensure software quality.  \nKeywords: antipattern; code smell; code smell detection; identification; machine learning algorithms  \n1. Introduction  \nCode smells can indicate deeper problems in the software, adversely affecting the software quality [1–5] . Th","cbCaisKRWNbaqErc","https://ap.wps.com/l/cbCaisKRWNbaqErc","pdf",4864967,1,37,"English","en",105,"# Introduction\n## Motivation and Background\n## ML-Based Approaches in Code Smell Detection","[{\"question\":\"What problem does code smell detection address?\",\"answer\":\"Code smell detection identifies software issues that can lead to deeper problems and future maintenance risks, helping improve software quality and reusability.\"},{\"question\":\"Which types of methods are covered, and what role do machine learning techniques play?\",\"answer\":\"The survey reviews multiple detection approaches and focuses on machine-learning-based methods, including common classifiers such as Support Vector Machine, J48, Naive Bayes, and Random Forest.\"},{\"question\":\"What obstacles or threats affect code smell detection in existing studies?\",\"answer\":\"Key challenges include the lack of standardized code smell definitions, difficulty in feature selection, and difficulties in handling large-scale datasets.\"}]","Machine Learning-Based Methods for Code Smell Detection - 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