A Large Language Model-Powered Agentic Framework for Interactive Coding Assistance and Automated Error Fixing
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I2P117Keywords:
MERN Stack, Large Language Models (LLM), Groq AI, Interactive Coding Assistance, Automated Error Fixing, Software Metrics, Intelligent Debugging, Code AnalysisAbstract
The inefficiencies in software development are still prevailing in modern software development, as developers are taking time in order to find syntax errors, warnings, logic errors, and security bugs in various programming languages. Conventional support systems are unable to offer much feedback, adaptive performance, and the balance between detection and automated correction. To address these issues, this paper proposes an agentic framework based on Large Language Models (LLMs) to provide interactive code assistance and auto error correction, with the implementation based on the MERN stack and multi-model optimization (e.g., Groq, LLaMA) to achieve better accuracy, response time, and token efficiency. The system allows any code written in Python, JavaScript, Java, C#, C++ to be submitted in a secure way, with real-time analysis, highlighting of issues, and proposed context-aware fixes, and making automatic corrections to the code performed by the LLM. Performance evaluation is excellent at detection with different success rates of correction and highest success rates in Python (91.6% success rate) and lower success rates in more complex languages like C++ (80%). Such measures as cyclomatic complexity, fix success ratio, token consumption, and variability of fix time (400ms-7688ms) are saved in MongoDB and displayed on React dashboards. Findings show that the framework has a high level of consistency in error detection and interactive feedback but more work is required to stabilize the correction efficiency and lower the variability of fix times. The significance of this system is in its flexibility in cross-language, real-time debugging, and built-in performance analysis, and has the potential to significantly shorten software development cycles, which is evident as a clear advantage over the old-fashioned tools of the traditional form of the static analysis.
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