Advancements in automated program repair: a comprehensive review

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dc.authorid0000-0002-1759-6045
dc.contributor.authorDikici, Sena
dc.contributor.authorBilgin, Turgay Tugay
dc.date.accessioned2026-02-08T15:14:56Z
dc.date.available2026-02-08T15:14:56Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThis review paper presents a comprehensive examination of automated program repair (APR) and its significant contribution to the field of modern software engineering. It elucidates how APR methodologies markedly mitigate manual debugging needs by automating the detection and resolution of software glitches. The study encompasses an in-depth exploration of three primary categories of APR techniques: template-based, machine learning, and deep learning approaches, drawing from an exhaustive evaluation of 41 APR tools. Each category showcases distinct strategies for managing diverse software errors, underscoring the breadth and effectiveness of current APR methodologies. Template-based APR solutions utilize pre-established patterns to efficiently tackle common coding issues, while machine learning-driven approaches dynamically devise repair strategies from historical bug-fix datasets. Deep learning methods extend error rectification boundaries by delving into the semantic context of code, yielding more precise adjustments. The ongoing advancement of APR technologies necessitates researchers to address critical challenges, including the integration of semantic-syntactic analyses, mitigation of data scarcity, optimization of cross-platform tools, development of context-aware approaches, enhancement of fault localization and patch validation processes, and establishment of standardized performance evaluation metrics. This comprehensive analysis underscores the pivotal role of APR in enhancing software efficiency and reliability, representing significant progress in software development and maintenance practices.
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK)
dc.description.sponsorshipOpen access funding provided by the Scientific and Technological Research Council of Turkiye (TUBITAK).
dc.identifier.doi10.1007/s10115-025-02383-9
dc.identifier.endpage4783
dc.identifier.issn0219-1377
dc.identifier.issn0219-3116
dc.identifier.issue6
dc.identifier.scopus2-s2.0-86000319077
dc.identifier.scopusqualityQ2
dc.identifier.startpage4737
dc.identifier.urihttps://doi.org/10.1007/s10115-025-02383-9
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5507
dc.identifier.volume67
dc.identifier.wosWOS:001438413400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofKnowledge and Information Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectAutomated program repair
dc.subjectSoftware bugs
dc.subjectMachine learning
dc.subjectDeep learning
dc.subjectCode patterns
dc.titleAdvancements in automated program repair: a comprehensive review
dc.typeReview Article

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