[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128704-en":3,"doc-seo-128704-105":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},128704,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A Systematic Analysis on Machine Learning Classifiers with Data Pre-processing to Detect Anti-pattern from Source Code - Research","Automatic detection of anti-patterns from source code lowers software maintenance costs and supports more reliable evolution. Machine learning is widely used for this task, but selecting an appropriate classifier and the right data pre-processing pipeline remains critical. This work presents a comparative analysis of 16 classifiers grouped into four categories, evaluating their effectiveness when combined with data pre-processing (DPP). Experiments use four common anti-patterns from three open-source Java projects, reporting top performance such as Dagging at 98.4% and kernel logistic regression at 97%.","IAES International Journal of Artificial Intelligence (IJ-AI)  \nVol. 14, No. 1, February 2025, pp. 376∼384  \nISSN: 2252-8938, DOI: 10.11591/ijai.v14.i1.pp376-384 ❒ 376  \n\n| A systematic analysis on machine learning classifiers with data pre-processing to detect anti-pattern from source code\u003Cbr>Nazneen Akhter1 , Afrina Khatun1 , Md. Sazzadur Rahman2 , A. S. M. Sanwar Hosen3 ,\u003Cbr>Mohammad Shahidul Islam2\u003Cbr>1Department of Computer Science and Engineering, Bangladesh University of Professionals, Dhaka, Bangladesh\u003Cbr>2Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh\u003Cbr>3Department of Artificial Intelligence and Big Data, Woosong University, Daejeon, South Korea |  |  |\n| --- | --- | --- |\n| Article Info\u003Cbr>Received Jan 16, 2024 Revised Jul 10, 2024 Accepted Jul 26, 2024\u003Cbr>Keywords:\u003Cbr>Anti-pattern Code quality\u003Cbr>Code smell\u003Cbr>Data pre-processing Machine learning |  | ABSTRACT\u003Cbr>Automatic detection of anti-patterns from source code can reduce software maintenance costs massively. Nowadays, machine learning approaches are very commonly used to identify anti-patterns. Hence, it is very crucial to choose aclassifier that can be useful for detecting anti-patterns. This work aims to help practitioners to choose a suitable classifier to detect anti-patterns. In this paper, we highlight 16 classifiers in four different categories to detect anti-patterns. Furthermore, the performance of these classifiers is identified with the data pre-processing (DPP) to detect four commonly occurring anti-patterns from the three commonly used open-source Java projects’ source code. The accuracy of Dagging classifiers is 98.4% . Kernel logistic regression (KLR) also performs well i.e., 97% . In the case of time complexity, naive Bayes (NB), decision trees (DT), support vector machines (SVM), library for support vector machines (LibSVM), logistic, and LightGBM (LB) have less time complexity to build a model in all the projects.\u003Cbr>This is an open access article under the CC BY-SA license. |\n| Corresponding Author: |  |  |\n| A. S. M. Sanwar Hosen\u003Cbr>Department of Artificial Intelligence and Big Data, Woosong University Daejeon 34606, South Korea\u003Cbr>Email: [sanwar@wsu.ac.kr](sanwar@wsu.ac.kr) |  |  |\n\n1. INTRODUCTION  \nSoftware quality is very important as it defines the ease of maintainability, testability, readability, stability, speed, usability, size, cost, and security. It also ensures the smoothness, conciseness, and customer satisfaction of software. However, due to the heavy workload and time pressure, software quality issues are usually ignored. Some reports show that software evolution, understandability, usability, modularity, reusability, analyzability, and changeability are neglected during the development process of the software [1] . As a result, design flaws i.e., poor structure in the source code increase the development and maintenance cost. These design flaws are termed as anti-pattern or code smell. Palomba et al. [2] report that the maintenance cost of software is 2–100 times greater than the development cost. The term code smell (anti-pattern) is first introduced in [3] . Anti-patterns from a source code can be detected both by manual and automated processes. For large software projects, the manual detection system is a very time-consuming process as it mainly depends on proper documentation, source code structure, and developer’s experience [1] . Hence, an automated and effective anti-pattern detection technique is essential. Although few studies are available in this field. Sometimes people may become confused about choosing machine learning (ML) classifiers and environmental  \nsetup. Several researchers introduced three different support vector machines (SVM)-based approaches [4]–[6] . Maiga et al. did not adopt any data pre-processing (DPP) technique, while Akhter et al. adopted a DPP technique namely synthetic minority over-sampling technique (SMOTE) with SVM which performed better than synthetic ","cbCaiffWfyWCqw5L","https://ap.wps.com/l/cbCaiffWfyWCqw5L","pdf",4048118,1,9,"English","en",105,"# Introduction\n# Literature Review on Anti-pattern Detection\n# Methodology and Analysis of ML Classifiers\n# Results and Execution\n# Conclusion and Future Research","[{\"question\":\"What is the goal of the paper regarding anti-pattern detection?\",\"answer\":\"The paper aims to help practitioners choose suitable machine learning classifiers to detect anti-patterns from source code, while clarifying how data pre-processing affects performance.\"},{\"question\":\"How many classifiers does the study analyze and how are they organized?\",\"answer\":\"The study highlights 16 classifiers, organized into four different categories for anti-pattern detection.\"},{\"question\":\"Which data pre-processing approach is used and how does it impact results?\",\"answer\":\"A data pre-processing technique (DPP) is applied to enhance classifier performance, and the paper evaluates classifier accuracy and time complexity under this setting.\"}]","A Systematic Analysis on Machine Learning Classifiers with Data Pre-processing to Detect Anti-pattern from Source Code - 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