LLM-ASSISTED AUTOMATED CODE REVIEW AND BUG PREDICTION FOR SOFTWARE QUALITY ENHANCEMENT

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

With the rapid growth of software systems, ensuring code quality and early bug detection has become increasingly challenging and time-consuming. Traditional code review processes rely heavily on manual inspection and rule-based static analysis tools, which often fail to scale and may miss complex or context-dependent defects. This paper proposes an LLM-Assisted Automated Code Review and Bug Prediction System that leverages large language models (LLMs) to analyse source code, identify potential bugs, security vulnerabilities, and code quality issues. The system combines natural language understanding with program analysis to learn coding patterns, detect anomalies, and predict defect-prone code segments at an early stage of development. Experimental evaluation demonstrates that LLM-assisted analysis enhances detection accuracy and supports developers with explainable, actionable recommendations, making it a promising solution for modern software engineering workflows.

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Keywords

Large Language Models (LLMs), Automated Code Review, Bug Prediction, Software Quality Assurance, Deep Learning, Static Analysis.