Integrating AI-Driven Automated Code Review in Agile Development: Benefits, Challenges, and Best Practices

( Vol-11,Issue-2,March - April 2025 )

Author(s): Saad Ahmed

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Page No: 01-10
DOI: 10.22161/ijaems.112.1

Keywords:

AI-powered code review, Agile software development, developer productivity, integration challenges, and best practices.

Abstract:

The integration of AI-powered automated code review tools has significantly transformed Agile software development by improving efficiency, maintaining coding standards, and enhancing developer productivity. These tools streamline repetitive tasks, identify potential issues early, and enforce consistency in code quality. However, their adoption comes with challenges such as accuracy constraints, difficulties in integrating with legacy systems, and hesitation among developers. This research employs a mixed-methods approach, combining qualitative and quantitative techniques to examine the benefits, challenges, and best practices associated with AI-driven code reviews. To gather insights, surveys and interviews were conducted with software engineers, DevOps professionals, and Agile practitioners. Additionally, real-world case studies analyzed how organizations have implemented AI-based code reviews, while an experimental study measured performance indicators such as error detection rates, review efficiency, and developer workflow improvements. The findings suggest that AI tools significantly reduce code review time, enhance consistency, and allow developers to concentrate on complex problem-solving rather than manual review processes. However, AI's inability to fully grasp context-sensitive issues, challenges in analyzing complex code logic, and resistance from developers remain notable barriers. Concerns about job security and loss of control over decision-making further contribute to adoption challenges. To overcome these issues, this study emphasizes the importance of a balanced approach where AI tools assist rather than replace human reviewers. Regular training and updates are crucial to improving AI accuracy and keeping pace with evolving coding practices. Gradual integration with existing systems can mitigate compatibility challenges, while transparent communication can help alleviate developer concerns. Additionally, establishing a validation mechanism, where human reviewers verify AI-generated recommendations, can enhance reliability and trust in these tools. In conclusion, while AI-driven automated code reviews offer substantial benefits for Agile teams, their successful implementation depends on strategic deployment, ongoing refinements, and a well-balanced collaboration between AI and human expertise. By following best practices, organizations can optimize AI-assisted code reviews, ultimately improving software quality and streamlining development workflows.

Received: 24 Jan 2025; Received in revised form: 26 Feb 2025; Accepted: 03 Mar 2025; Available online: 08 Mar 2025

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