[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123180-en":3,"doc-seo-123180-105":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},123180,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A Survey of Predicting Software Reliability Using Machine Learning Methods","The article surveys approaches for predicting software reliability with machine learning and deep learning methods, emphasizing the practical need for flawless, maintainable software in domains such as medicine and industrial control. It discusses how modern testing and debugging rely on historical failure data and how learning algorithms can model fault behavior to support reliable predictions. It also reviews intelligent frameworks, including deep learning’s role in extracting insights from requirements text, generating source code patterns, and forecasting software errors to improve quality.","A survey of predicting software reliability using machine  \nlearning methods  \nShahbaa I. Khaleel, Lumia Faiz Salih  \nDepartment of Software, College of Computer Science and Mathematics, Mosul University, Mosul, Iraq  \n\n| Article history:\u003Cbr>Received Nov 27, 2022 Revised Feb 9, 2023 Accepted Mar 10, 2023 |\n| --- |\n| Keywords:\u003Cbr>Artificial intelligence Deep learning Machine learning Prediction Software reliabilty |\n\nCorresponding Author:  \nIn light of technical and technological progress, software has become an urgent need in every aspect of human life, including the medicine sector and industrial control. Therefore, it is imperative that the software always works flawlessly. The information technology sector has witnessed a rapid expansion in recent years, as software companies can no longer rely only on cost advantages to stay competitive in the market, but programmers must provide reliable and high-quality software, and in order to estimate and predict software reliability using machine learning and deep learning, it was introduced A brief overview of the important scientific contributions to the subject of software reliability, and the researchers' findings of highly efficient methods and techniques for predicting software reliability.  \nThis is an open access article under the CC BY-SA license.  \nShahbaa I. Khaleel  \nDepartment of Software, College of Computer Science and Mathematics, Mosul University Mosul, Iraq  \nEmail: [shahbaaibrkh@uomosul.edu.iq](shahbaaibrkh@uomosul.edu.iq)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nCompanies create smart software to increase software credibility, and thus control failures. Since software in general has real concerns about reliability and maintainability. Researchers have used a variety of machine learning algorithms to find controls for variables that have an impact on most programs [1], [2] .  \nCurrently, testing methods are important and most important in determining the usability of software [3] . Software usability is defined as the ability to use the software to its fullest potential without errors within a predetermined period of time [4] . Various techniques are on hand to generate clever programs. Artificial neural networks, fuzzy set theory, approximate set theory, and artificial intelligence are all examples of records retrieval [5] . Some errors formed during error removal and some errors initially present in the data set have the potential to cause the entire system to fail [6] .  \nAccording to 􀜣3 , the framework shown in Figure 1 of applying program usability, algorithm, and architecture to the reliable work of software without defects. Intelligent software becomes necessary by combining machine learning techniques on the defective company dataset to build reliable models in different dimensions. Based on the early prediction model [7] .  \nIncidents that turn into critical failures as a result of program failure cause financial losses, time losses and information losses [8] . For this reason, errors are handled correctly at the time of release, and they are carefully checked throughout the testing and debugging processes using historical data about software failures to determine the number of test-related errors. Based on the failure history of the application, the best defect handling methods m (t) and software density function λ (t) are discovered for software reliability models [9] .  \nMachine learning is imperative to flaw detection. It is used in evaluating software program reliability to seem to be for refined variations in how nicely a product works in proper use, and it makes uses a variety of  \nmachine learning techniques to validate a prediction application [10] . Depending on the variety of processing layers via which the facts need to pass, the identify \"deep\" was once given, and deep studying led to the introduction of neural networks with higher complexity and greater wonderful mastering capabilities, the place the statistical mannequin is produced as ou","cbCaihlX96DUghfV","https://ap.wps.com/l/cbCaihlX96DUghfV","pdf",420948,1,10,"English","en",105,"# ABSTRACT\n# INTRODUCTION\n## Software reliability and maintainability motivation\n## Testing, usability, and error handling\n## Machine learning and deep learning for prediction\n## Deep learning in software engineering processes","[{\"question\":\"Why is software reliability prediction important in the article?\",\"answer\":\"Reliable software is treated as essential to avoid failures that lead to financial, time, and information losses, especially in critical sectors like medicine and industrial control.\"},{\"question\":\"What role do historical software failure data play?\",\"answer\":\"The article states that using failure history helps determine suitable defect handling methods and derive parameters used in software reliability models.\"},{\"question\":\"How does deep learning contribute to predicting software errors according to the document?\",\"answer\":\"Deep learning models analyze large datasets of code to find patterns related to potential bugs or vulnerabilities, enabling proactive identification before issues become critical.\"}]","A Survey of Predicting Software Reliability Using Machine Learning Methods | 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is software reliability prediction important in the article?","Question",{"text":75,"@type":76},"Reliable software is treated as essential to avoid failures that lead to financial, time, and information losses, especially in critical sectors like medicine and industrial control.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role do historical software failure data play?",{"text":80,"@type":76},"The article states that using failure history helps determine suitable defect handling methods and derive parameters used in software reliability models.",{"name":82,"@type":73,"acceptedAnswer":83},"How does deep learning contribute to predicting software errors according to the document?",{"text":84,"@type":76},"Deep learning models analyze large datasets of code to find patterns related to potential bugs or vulnerabilities, enabling proactive identification before issues become 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