[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125243-en":3,"doc-seo-125243-105":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":4,"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},125243,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Describe The Process Of Improving Software Fault Prediction Through The Use Of Hybrid Machine Learning Algorithms","Guaranteeing software dependability and quality depends on Software Fault Prediction (SFP), which identifies likely defects before deployment to reduce maintenance costs and improve reliability. Traditional models often struggle with data imbalance, weak generalization, and difficulty handling high-dimensional metrics. This research proposes a hybrid framework combining deep learning and ensemble learning, specifically DNN and Gradient Boosting Decision Trees, strengthened by feature selection via RFE and Mutual Information. Experiments on NASA MDP and PROMISE datasets show improved performance, including 92% accuracy, 91% generalization, and a low false-positive rate of 7%.","Describe The Process Of Improving Software Fault Prediction Through The Use Of Hybrid Machine Learning Algorithms  \nSEEJPH Volume XXVI, S2,2025, ISSN: 2197-5248; Posted:03-02-25  \nDescribe The Process Of Improving Software Fault Prediction Through The Use Of Hybrid Machine Learning Algorithms  \nShipra Goel1*  \n1*Assistant Professor, Department ofcomputer science, Indira Gandhi Institute of Management and Technology,  \nBallabhgarh, Faridabad, Haryana 121004, India. Email id: [shipragoelred@gmail.com](shipragoelred@gmail.com)  \n*Corresponding author: Shipra Goel  \n*Email id: [shipragoelred@gmail.com](shipragoelred@gmail.com)  \nKEYWORDS  \nSoftware Fault Prediction, Hybrid Machine Learning, Ensemble Learning, Deep Neural Networks, Feature Selection, Software Quality Assurance, Defect Detection.  \nABSTRACT  \nIn contemporary software development, it is imperative to guarantee the dependability and quality of software. The identification of potential defects in software systems prior to deployment is a critical function of Software Fault Prediction (SFP), which in turn reduces maintenance costs, improves reliability, and improves the overall quality of software. Although conventional defect prediction models are somewhat effective, they frequently encounter issues such as data imbalance, poor generalization, and the inability to manage high-dimensional software metrics. This research suggests a hybrid machine learning framework that incorporates feature selection techniques, deep learning, and ensemble learning to improve the prediction of software faults in order to overcome these challenges. The hybrid model that has been proposed combines Deep Neural Networks (DNN) and Gradient Boosting Decision Trees (GBDT) to capitalize on their respective strengthsin the learning of complex data patterns and the generation of reliable predictions. In order to enhance the efficacy of the model by reducing dimensionality and eliminating redundant features, feature selection methods such as Recursive Feature Elimination (RFE) and Mutual Information (MI) are implemented. The model is trained and evaluated using publicly available NASA MDP and PROMISE repository datasets, which include core software metrics such as lines of code (LOC), cyclomatic complexity, coupling, and cohesiveness. The hybrid model outperforms conventional machine learning classifiers, such as Support Vector Machines (SVM), Random Forest, and Gradient Boosting, across a variety of performance metrics, as evidenced by experimental results. The hybrid model considerably improves the reliability of software fault detection by achieving a 92% accuracy, 91% generalization score, and a low false positive rate of 7%. Additionally, it maintains a high F1-score (90.5%) andAUC-ROC (93%), which guarantees enhanced precision and recall in the identification of failed software components. The hybrid machine learning framework that has been proposed improves scalability, reduces misclassification errors, and enhances defect prediction accuracy by incorporating adaptive learning, feature selection, and ensemble techniques. The results of this study indicate that hybrid models are a valuable instrument for software quality assurance and defect management in real-world applications, as they can effectively address critical challenges in software fault prediction.  \n1. Introduction  \nEnsuring software dependability and quality takes first priority in the always changing terrain of software development. Detecting and fixing software flaws before release has become a major difficulty as contemporary applications get ever more complicated [1] . Ignorance of software flaws could have serious repercussions including system failures, security risks, and financial losses [2] . Using machine learning methods to find possible flaws in software systems, software fault prediction (SFP) has become a critical topic of research addressing these difficulties. Although useful to some degree, traditional defect p","cbCainZ5YJV5PwWb","https://ap.wps.com/l/cbCainZ5YJV5PwWb","pdf",474550,1,16,"English","en",105,"# Introduction\n## Definition and Importance of Software Fault Prediction\n# Hybrid Machine Learning Framework\n## Feature Selection and Model Components\n# Datasets and Evaluation Results\n## Performance Metrics and Comparisons","[{\"question\":\"What problems do traditional defect prediction models face, according to the research?\",\"answer\":\"They often encounter data imbalance, poor generalization, and difficulty managing high-dimensional software metrics.\"},{\"question\":\"What hybrid approach is proposed to improve software fault prediction?\",\"answer\":\"The framework combines Deep Neural Networks (DNN) and Gradient Boosting Decision Trees (GBDT) using ensemble ideas to better learn complex patterns and produce reliable predictions.\"},{\"question\":\"How does feature selection contribute to the proposed model?\",\"answer\":\"Feature selection reduces dimensionality and removes redundant features using methods such as Recursive Feature Elimination (RFE) and Mutual Information (MI).\"}]","Describe The Process Of Improving Software Fault Prediction Through The Use Of Hybrid Machine Learning Algorithms | PDF",1785897683,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"describe-the-process-of-improving-software-fault-prediction-through-the-use-of-hybrid-machine-learning-algorithms","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/describe-the-process-of-improving-software-fault-prediction-through-the-use-of-hybrid-machine-learning-algorithms/125243/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problems do traditional defect prediction models face, according to the research?","Question",{"text":75,"@type":76},"They often encounter data imbalance, poor generalization, and difficulty managing high-dimensional software metrics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What hybrid approach is proposed to improve software fault prediction?",{"text":80,"@type":76},"The framework combines Deep Neural Networks (DNN) and Gradient Boosting Decision Trees (GBDT) using ensemble ideas to better learn complex patterns and produce reliable predictions.",{"name":82,"@type":73,"acceptedAnswer":83},"How does feature selection contribute to the proposed model?",{"text":84,"@type":76},"Feature selection reduces dimensionality and removes redundant features using methods such as Recursive Feature Elimination (RFE) and Mutual Information (MI).","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]