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AI-Powered Code Review Enhancing Software Quality with Intelligent Agents

Author(s) Ravikanth Konda
Country United States
Abstract The constantly changing world of software development requires incessant advancements in quality control measures. Code review, the essential practice for detecting bugs, imposing coding standards, and maintaining software with ease, has historically been based on traditional methods. Yet, the conventional method is usually labor-intensive, irregular, and prone to human error. With the introduction of Artificial Intelligence (AI), code review is facing a revolutionary change. AI-based code review tools leverage intelligent agents like machine learning algorithms, AI-fortified static analysis tools, and large language models (LLMs) to automatically identify defects, propose improvements, and apply best practices. The tools can interpret code semantics, learn from past code changes, and predict upcoming defects. This paper presents a detailed examination of AI-driven code review mechanisms and their software quality implications. We investigate recent developments in the area, compare different tools and frameworks, and examine the effectiveness of AI in detecting bugs, improving readability, and enhancing code maintainability. Through a mix of literature review, experimental assessment, and developer feedback, this research illustrates how AI-driven code review systems play a critical role in improved software quality, accelerated development cycles, and lower technical debt. The conversation also covers the shortcomings of existing systems, ethical implications, and possible future developments.
Field Engineering
Published In Volume 4, Issue 3, March 2023
Published On 2023-03-03
Cite This AI-Powered Code Review Enhancing Software Quality with Intelligent Agents - Ravikanth Konda - IJLRP Volume 4, Issue 3, March 2023. DOI 10.70528/IJLRP.v4.i3.1541
DOI https://doi.org/10.70528/IJLRP.v4.i3.1541
Short DOI https://doi.org/g9hndn
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