CodeWiki: Evaluating AI's Ability to Generate Holistic Documentation for Large-Scale Codebases
For software engineers maintaining large codebases, this work provides a practical tool for generating holistic documentation, though the improvement over baseline is incremental.
CodeWiki introduces a framework for automated repository-level documentation that captures cross-file and system-level interactions, achieving a 68.79% quality score with proprietary models, outperforming the DeepWiki baseline by 4.73%.
Given a large and evolving codebase, the ability to automatically generate holistic, architecture-aware documentation that captures not only individual functions but also cross-file, cross-module, and system-level interactions remains an open challenge. Comprehensive documentation is essential for long-term software maintenance and collaboration, yet current automated approaches still fail to model the rich semantic dependencies and architectural structures that define real-world software systems. We present \textbf{CodeWiki}, a unified framework for automated repository-level documentation across seven programming languages. CodeWiki introduces three key innovations: (i) hierarchical decomposition that preserves architectural context across multiple levels of granularity, (ii) recursive multi-agent processing with dynamic task delegation for scalable generation, and (iii) multi-modal synthesis that integrates textual descriptions with visual artifacts such as architecture diagrams and data-flow representations. To enable rigorous evaluation, we introduce \textbf{CodeWikiBench}, a comprehensive benchmark featuring multi-dimensional rubrics and LLM-based assessment protocols. Experimental results show that CodeWiki achieves a 68.79\% quality score with proprietary models, outperforming the closed-source DeepWiki baseline (64.06\%) by 4.73\%, with particularly strong improvements on high-level scripting languages (+10.47\%). We open-source CodeWiki to foster future research and community adoption.