A Decade of Progress: A Systematic Literature Review on the Integration of AI in Software Engineering Phases and Activities (2013-2023)

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dc.authoridOZTURK, Muhammed Maruf/0000-0001-6446-9754
dc.authoridKabakus, Abdullah Talha/0000-0003-2181-4292
dc.authoridADAK, M. Fatih/0000-0003-4279-0648
dc.authoridSaleh, Mohammed/0000-0002-8142-6323
dc.authoridAkpinar, Mustafa/0000-0003-4926-3779
dc.authoridDurrani, Dr. Usman/0000-0003-4255-6253
dc.contributor.authorDurrani, Usman Khan
dc.contributor.authorAkpinar, Mustafa
dc.contributor.authorAdak, Muhammed Fatih
dc.contributor.authorKabakus, Abdullah Talha
dc.contributor.authorOzturk, Muhammed Maruf
dc.contributor.authorSaleh, Mohammed
dc.date.accessioned2025-10-11T20:48:15Z
dc.date.available2025-10-11T20:48:15Z
dc.date.issued2024
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThe synergy between software engineering (SE) and artificial intelligence (AI) catalyzes software development, as numerous recent studies illustrate an intensified intersection between these domains. This systematic literature review examines the integration of AI techniques or methodologies across SE phases and related activities spanning from 2013 to 2023, resulting in the selection of 110 research papers. Investigating the profound influence of AI techniques, including machine learning, deep learning, natural language processing, optimization algorithms, and expert systems, across various SE phases-such as planning, requirement engineering, design, development, testing, deployment, and maintenance-is the focal point of this study. Notably, the extensive adoption of machine learning and deep learning algorithms in the development and testing phases has enhanced software quality through defect prediction, code recommendation, and vulnerability detection initiatives. Furthermore, natural language processing's role in automating requirements classification and sentiment analysis has streamlined SE practices. Optimization algorithms have also demonstrated efficacy in refining SE activities such as feature location and software repair action predictions, augmenting precision and efficiency in maintenance endeavors. Prospective research emphasizes the imperative of interpretable AI models and the exploration of novel AI paradigms, including explainable AI and reinforcement learning, to promote ethical and efficient software development practices. This paper fills the gap identified in AI techniques dedicated to improving SE phases. The review concludes that AI in SE is revolutionizing the discipline, enhancing software quality, efficiency, and innovation, with ongoing efforts targeting the mitigation of identified limitations and the augmentation of AI capabilities for intelligent and dependable SE.en_US
dc.identifier.doi10.1109/ACCESS.2024.3488904
dc.identifier.endpage171204en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85208384243en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage171185en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3488904
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21833
dc.identifier.volume12en_US
dc.identifier.wosWOS:001362127900018en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectAIen_US
dc.subjectartificial intelligenceen_US
dc.subjectdeep learningen_US
dc.subjectexpert systemsen_US
dc.subjectintegrationen_US
dc.subjectmachine learningen_US
dc.subjectnatural language processingen_US
dc.subjectoptimization algorithmsen_US
dc.subjectplanningen_US
dc.subjectrequirement engineeringen_US
dc.subjectsoftware deploymenten_US
dc.subjectsoftware developmenten_US
dc.subjectsoftware engineeringen_US
dc.subjectsoftware maintenanceen_US
dc.subjectsoftware testingen_US
dc.subjectsystematic literature reviewen_US
dc.subjectnatural language processingen_US
dc.subjectoptimization algorithmsen_US
dc.subjectplanningen_US
dc.subjectrequirement engineeringen_US
dc.subjectsoftware deploymenten_US
dc.subjectsoftware developmenten_US
dc.subjectsoftware engineeringen_US
dc.subjectsoftware maintenanceen_US
dc.subjectsoftware testingen_US
dc.subjectsystematic literature reviewen_US
dc.titleA Decade of Progress: A Systematic Literature Review on the Integration of AI in Software Engineering Phases and Activities (2013-2023)en_US
dc.typeReviewen_US

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