[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121266-en":3,"doc-seo-121266-105":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},121266,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","A Systematic Literature Review on the Use of Machine Learning in Software Engineering","Software engineering (SE) spans multiple phases required to build sustainable software systems. Machine learning (ML), an AI branch, has gained momentum for its ability to analyze large datasets and discover useful patterns. This review addresses a research gap by examining primary studies that apply ML techniques within SE processes. It investigates the current state of the art through explicit objectives and research questions, mapping ML applications across quality assurance, maintenance, comprehension, and documentation, and summarizing major learning paradigms such as supervised, unsupervised, and deep learning.","A Systematic Literature Review on the Use of Machine Learning in Software Engineering  \n[Nyaga Fred ](Nyaga Fred m156796@edu.misis.ru)[m156796@edu.misis.ru](Nyaga Fred m156796@edu.misis.ru),  \nTemkin, I.O.  \nNational University of Science and Technology “MISiS”, Moscow, 119049, Russia  \nAbstract  \nSoftware engineering (SE) is a dynamic field that involves multiple phases all of which are necessary to develop sustainable software systems. Machine learning (ML), a branch of artificial intelligence (AI), has drawn a lot of attention in recent years thanks to its ability to analyze massive volumes of data and extract useful patterns from data. Several studies have focused on examining, categorising, and assessing the application of ML in SE processes. We conducted a literature review on primary studies to address this gap. The study was carried out following the objective and the research questions to explore the current state of the art in applying machine learning techniques in software engineering processes. The review identifies the key areas within software engineering where ML has been applied, including software quality assurance, software maintenance, software comprehension, and software documentation. It also highlights the specific ML techniques that have been leveraged in these domains, such as supervised learning, unsupervised learning, and deep learning.  \nKeywords: machine learning, deep learning, software engineering, natural language processing, source code.  \nSLR Machine Learning Applications in SE  \nSECTION 1  \nIntroduction  \nSoftware engineering (SE) is a rapidly changing field that involves designing, developing, maintaining, testing and evolving software systems in a systematic and controlled manner. Machine learning (ML), a subfield of artificial intelligence (AI), has received substantial attention in recent years due to its potential to analyse enormous volumes of data. This has led to the exploration of its potential applications in software engineering, including but not limited to defect detection, code quality assessment, requirement analysis, and software project management. In today's SE landscape, the demand for high-quality and maintainable source code is paramount. Software applications are becoming increasingly complex, developers are facing challenges in writing efficient and errorfree code. Recent research shows that ML techniques can be seamlessly integrated into the software development process offering solutions to these challenges Pradel M. et al. (2018 .  \nAdvances in deep learning (DL) and natural language processing (NLP) have laid the foundation for the usage of machine learning tools and techniques to perform an extensive range of programming tasks. DL, a subset of ML, has revolutionised several industries such as language processing, image processing, text [translation and generation Beese D. et](translation and generation Beese D. et) al. (2023). In the context of SE, natural language processing (NLP) can be used to perform text-related tasks including software documentation, bug finding, code completion, and code translation, greatly boosting developer productivity and code quality Pauzi et al. (2023) . Conversely, image processing techniques can be utilised for activities such as visual testing, image recognition, user interface design, and enhancing the user experience within software systems Chen J.S. (2022) . The integration of these technologies not only streamlines software development processes but also improves the functionality and usability of software applications.  \nProgrammers have long depended on traditional approaches (such as compilers) to analyse code; these techniques use accuracy and logical reasoning to understand and manipulate software Nielson [F. et](F. et) al. (2015). However, as software systems become more  \nSLR Machine Learning Applications in SE  \nsophisticated, such approaches become less effective in solving particular issues. Existing software in SE mu","cbCairrp5UX38izV","https://ap.wps.com/l/cbCairrp5UX38izV","pdf",352431,1,28,"English","en",105,"# Introduction\n# Related work","[{\"question\":\"What is the purpose of the systematic literature review on ML in software engineering?\",\"answer\":\"The review aims to analyze current research advancements in applying machine learning to software engineering and to identify challenges, opportunities, and gaps in the existing landscape.\"},{\"question\":\"Which software engineering areas are identified as key application domains for ML?\",\"answer\":\"The review highlights applications in software quality assurance, software maintenance, software comprehension, and software documentation.\"},{\"question\":\"What kinds of machine learning techniques does the review emphasize for these SE domains?\",\"answer\":\"The review summarizes supervised learning, unsupervised learning, and deep learning as the major ML techniques leveraged across the identified domains.\"}]","A Systematic Literature Review on the Use of Machine Learning in Software Engineering | 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is the purpose of the systematic literature review on ML in software engineering?","Question",{"text":75,"@type":76},"The review aims to analyze current research advancements in applying machine learning to software engineering and to identify challenges, opportunities, and gaps in the existing landscape.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which software engineering areas are identified as key application domains for ML?",{"text":80,"@type":76},"The review highlights applications in software quality assurance, software maintenance, software comprehension, and software documentation.",{"name":82,"@type":73,"acceptedAnswer":83},"What kinds of machine learning techniques does the review emphasize for these SE domains?",{"text":84,"@type":76},"The review summarizes supervised learning, unsupervised learning, and deep learning as the major ML techniques leveraged across the identified 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