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@article{191793,
        author = {Nishikant Toshniwal and Prof. Mrs. Mrunal Buchade and Tanisha Gundecha and Vasudha Jagtap},
        title = {AI-Powered Interpreter for GitHub Repositories: Automated Code Understanding and Documentation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {9054-9061},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191793},
        abstract = {As open-source software continues to grow rapidly, GitHub has become a prominent place to host code repositories. However, a lot of repositories aren't well documented, maintained highly consistently, or made more easily configurable. While repository and source code reusability are key for fostering research and industry applications of code, many situations limit potential reuse of existing open-source code. In academia and industry, the state of the software code and change history (where applicable) is crucial to successfully understanding, modifying, or studying software. Recent studies take this further, showing that a repository-level understanding of source code (versus file or function level) is crucial for addressing challenges in software maintenance, software evolution, and for discovering new, novel, and more useful software innovations. Key advances in exploring repository-level source code evolve how developers benefit from artificial intelligence-enhanced code. Enhancements include (i) tracking software development (repository-level code graphs and commit history mining; e.g., seeing how code changed), (ii) acquiring and learning code (semantic code summarization and recommendation systems to find and use useful repositories and code), (iii) improving code quality (commit history mining, recommendation systems, and refactoring pipelines for detecting breaking changes and data clumps), and (iv) using LLMs that have trained on repositories doing repository-wide contextualized code completion and, repository-wide hierarchical summarization of code (benefiting users in addition to file or function level Q&A). Overall, these suggestions show how repository analysis driven by AI can greatly improve the onboarding effort, make code more accessible to modify and reuse in both academia and industry, and provide strong spaces to connect the two models for improving open-source software and open science.},
        keywords = {Level Code Analysis, Software Maintenance, Code Summarization, Commit History Mining, Dependency Graphs, Code Recommendation Systems, Refactoring Pipelines Software Reusability, Open-Source Software Engineering.},
        month = {January},
        }