Automating Code Review Systems Using Natural Language Processing
DOI:
https://doi.org/10.56472/ICCSAIML25-165Keywords:
Code review automation, Natural Language Processing (NLP), Software development, Code summarization, Text classification, Code analysis, NLP in software engineering, Machine learning for code review, Software quality assuranceAbstract
The increasing complexity of software systems has made code reviews a critical part of the software development process. However, traditional manual code review methods are often time-consuming and prone to human error, leading to inefficiencies in development workflows. This paper explores the potential of automating the code review process using Natural Language Processing (NLP) techniques. By leveraging NLP models, such as code summarization and text classification, it is possible to enhance the speed and accuracy of code review, reducing the burden on developers while maintaining high-quality standards. This paper reviews existing literature, discusses challenges associated with automating code reviews, and proposes a methodology for implementing NLP-based code review systems. The effectiveness of these systems is evaluated based on various criteria, including accuracy, scalability, and adaptability across different programming languages. The findings suggest that NLP can play a key role in the automation of code review processes, providing significant benefits to software development teams
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