[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120803-en":3,"doc-seo-120803-105":30,"detail-sidebar-cat-0-en-105":91},{"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},120803,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Software Defect Prediction Based on Optimized Machine Learning Models - A Comparative Study","Software defect prediction supports detecting potential defects before they appear during software development. Machine learning methods are increasingly applied, yet their performance can change with dataset characteristics and model hyperparameters, making tuning and feature selection difficult. This study evaluates multiple traditional models optimized for software defect prediction on NASA MDP datasets. Hyperparameters are fine-tuned via random search, feature dimensionality is reduced using PCA, and class imbalance is addressed with SMOTE. Results show optimized models improve classification accuracy, with k-NN performing best on several datasets and other models effective in specific cases.","Software Defect Prediction Based on Optimized Machine Learning Models: A Comparative Study  \nMuhammad Zain Fawwaz Nuruddin Siswantoro1*, Umi Laili Yuhana2  \n1,2 Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Jawa Timur Email:1* [6025222009@mhs.its.ac.id](6025222009@mhs.its.ac.id), [2](2 yuhana@if.its.ac.id)[ yuhana@if.its.ac.id](2 yuhana@if.its.ac.id)  \n(Naskah masuk: 24 Mei 2023, direvisi: 18 Jun 2023, diterima: 21 Jun 2023)  \nAbstract  \nSoftware defect prediction is crucial used for detecting possible defects in software before they manifest. While machine learning models have become more prevalent in software defect prediction, their effectiveness may vary based on the dataset and hyperparameters of the model. Difficulties arise in determining the most suitable hyperparameters for the model, as well as identifying the prominent features that serve as input to the classifier. This research aims to evaluate various traditional machine learning models that are optimized for software defect prediction on NASA MDP (Metrics Data Program) datasets. The datasets were classified using k-nearest neighbors (k-NN), decision trees, logistic regression, linear discriminant analysis (LDA), single hidden layer multilayer perceptron (SHL-MLP), and Support Vector Machine (SVM) . The hyperparameters of the models were fine-tuned using random search, and the feature dimensionality was decreased by utilizing principal component analysis (PCA) . The synthetic minority oversampling technique (SMOTE) was implemented to oversample the minority class in order to correct the class imbalance. k-NN was found to be the most suitable for software defect prediction on several datasets, while SHL-MLP and SVM were also effective on certain datasets. It is noteworthy that logistic regression and LDA did not perform as well asthe other models. Moreover, the optimized models outperform the baseline models in terms of classification accuracy. The choice of model for software defect prediction should be based on the specific characteristics of the dataset. Furthermore, hyperparameter tuning can improve the accuracy of machine learning models in predicting software defects.  \nKeywords: Machine Learning Models, Software Defect Prediction, Random Search, Principal Component Analysis, Hyperparameter Tuning.  \nI. INTRODUCTION  \nAs technology has advanced and consumer expectations for software have risen, the software development process has gotten increasingly intricate [1] . As a result, software engineers must now focus on improving their ability to detect and prevent software defects [2] . Software Defect Prediction (SDP) is a crucial technique that identifies potential software defects before they occur. In software engineering, SDP is an important and challenging task. Better software quality and reduced development costs are both linked to early defect detection in software development [3], [4] .  \nRecently, machine learning models have been widely used to detect defects in software. This is because machine learning models have the ability to find patterns automatically from data by recognizing defects in software [5] . Predicting software defects using machine learning models has been demonstrated to be useful in several studies, such as decision tree (DT) [6], Naïve Bayes (NB) [7], K-nearest Neighbors (k-NN) [8], [9], Artificial Neural Network (ANN) [10], and Support Vector  \nMachine (SVM) [11] . Different datasets and model hyperparameters can result in widely varying model performances in machine learning. A common challenge in machine learning is selecting the optimal model hyperparameters. However, almost all studies in SDP using machine learning models did not perform hyperparameter tuning to obtain the optimal model hyperparameters.  \nAnother issue in SDP using a machine learning model is selecting prominent features to use as input to the classifier. The optimal feature subset has been chosen to use several feature selection ","cbCaieGFECuZwvgv","https://ap.wps.com/l/cbCaieGFECuZwvgv","pdf",283210,1,7,"English","en",105,"# Introduction\n# Literature Review\n# Methods\n# Results and Discussion\n# Conclusion","[{\"question\":\"What problem does the study address in software defect prediction?\",\"answer\":\"The study addresses how machine learning performance varies with dataset characteristics and model hyperparameters, and how suitable inputs and features are selected for classifiers.\"},{\"question\":\"Which datasets and models are used for evaluation?\",\"answer\":\"The models are evaluated on NASA MDP (Metrics Data Program) datasets, using k-NN, decision trees, logistic regression, linear discriminant analysis, single hidden layer MLP, and SVM.\"},{\"question\":\"How are hyperparameters, features, and class imbalance handled in the proposed approach?\",\"answer\":\"Hyperparameters are fine-tuned with random search, features are dimensionality-reduced with PCA, and SMOTE is used to oversample the minority class to correct class imbalance.\"}]","Software Defect Prediction Based on Optimized Machine Learning Models - 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