Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Over the years, as the software systems grew in complexity and connectivity, the risk of getting a vulnerability embedded in a piece of code has also significantly grown. Secure code review—a contemporary practice in software development—becomes the first and last line in defense of the cyberworld. Traditional code reviewing methods, which are attacked by way of static and dynamic analyses, have been useful but limited due to scaling issues, the false positives they cannot avoid, and the inability to adapt to changing threat landscapes. The review herein aims to elucidate the transformation of secure code review with AI-powered enhancement of both static and dynamic analysis techniques. AI-powered models, chiefly those of ML and NLP genres, have been contributing toward discovering vulnerabilities accurately, minimizing false-positive cases, and interpreting codes into further insights about behaviors.
"SECURE CODE REVIEW WITH AI: ENHANCING STATIC AND DYNAMIC ANALYSIS", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.b468-b475, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507168.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator