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@article{195149,
        author = {K.CHANDRASEKAR and D. Revathy},
        title = {REPOSITORY QUESTION ANSWERING SYSTEM WITH LLM INTERFACE FOR CODEBASES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {7250-7256},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195149},
        abstract = {The complexity of managing and comprehending big codebases has increased due to the increasing growth of software development. The Local AI-Powered Coding Assistant shown in this project is intended to increase developer productivity by facilitating intelligent natural language interaction with source code. In order to carry out intricate coding tasks like code analysis, debugging, and multi-file updates, the system incorporates a React-based agent framework that combines reasoning, action, and observation. The suggested solution offers flexibility for various user processes by utilizing a FastAPI-based backend and supporting both web and command-line interfaces. In addition to enabling smooth interaction with both local and remote (GitHub) repositories, it integrates sophisticated code intelligence functions including repository analysis, bug identification, and security evaluation. By integrating a modular tool execution layer that permits integration with a variety of development utilities, including file operations, version control systems, and search techniques, the system highlights extensibility. privacy by providing users with control over their data and computing resources.},
        keywords = {Artificial Intelligence, Code Intelligence, Software Engineering, ReAct Agent, Code Analysis, Code Review, Natural Language Processing, Developer Assistant, FastAPI, Large Language Models (LLMs), Repository Management, GitHub Integration, Automation, Human-Computer Interaction, AI-Powered Development Tools.},
        month = {March},
        }