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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21686
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Total Authors : 22487
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| Paper Title: | code quality analysis using machine learing |
| Authors Name: | kawthar muhammad , senthil kumar |
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| Author Reg. ID: |
IJRTI_203992
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| Published Paper Id: | IJRTI2505319 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | |
| Abstract: | Abstract Ensuring high-quality software code is a critical aspect of software development, as poor code quality can lead to security vulnerabilities, maintainability challenges, and increased technical debt. Traditional methods of code quality analysis, such as manual code reviews and rule-based static analysis tools, often fail to scale effectively, especially in large and complex codebases. With the rapid advancements in artificial intelligence and machine learning, automated code quality analysis has emerged as a promising solution to enhance software reliability and maintainability. This paper explores the application of machine learning algorithms in automated code quality analysis, focusing on how these techniques can improve defect detection, code smell identification, and maintainability assessment. We discuss various supervised and unsupervised learning approaches, including deep learning models, decision trees, and ensemble methods, which have demonstrated significant potential in learning patterns from historical code repositories. By leveraging large datasets of annotated code samples, these models can predict quality issues with higher accuracy than traditional static analysis techniques. |
| Keywords: | machine learning, code quality, defect detection, static analysis, maintainability, automation , |
| Cite Article: | "code quality analysis using machine learing", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.d141-d142, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505319.pdf |
| Downloads: | 000386 |
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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 |
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Published Paper ID: IJRTI2505319
Registration ID:203992
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: d141-d142 Country: kano, kano , Nigeria Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505319 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505319 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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