@inproceedings{hoang-etal-2026-codewiki,
title = "{C}ode{W}iki: Evaluating {AI}{'}s Ability to Generate Holistic Documentation for Large-Scale Codebases",
author = "Hoang, Anh Nguyen and
Le-Anh, Minh and
Le, Bach and
Bui, Nghi D. Q.",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.288/",
pages = "5812--5827",
ISBN = "979-8-89176-395-1",
abstract = "Comprehensive software documentation is crucial yet costly to produce. Despite recent advances in large language models (LLMs), generating holistic, architecture-aware documentation at the repository level remains challenging due to complex and evolving codebases that exceed LLM context limits. Existing automated methods struggle to capture rich semantic dependencies and architectural structure. We present $\textbf{CodeWiki}$, a unified framework for automated repository-level documentation across seven mainstream programming languages. CodeWiki combines top-down hierarchical decomposition with a divide-and-conquer agent system to preserve architectural context and scale documentation generation, and a bottom-up synthesis that integrates textual descriptions with visual artifacts such as architecture and data-flow diagrams. We also introduce $\textbf{CodeWikiBench}$, a benchmark with hierarchical rubrics and LLM-based evaluation protocols. Experiments show that CodeWiki achieves a 68.79{\%} quality score with proprietary models, outperforming the closed-source DeepWiki baseline by 4.73{\%}, with especially strong gains on scripting languages. CodeWiki is released as open source to support future research."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hoang-etal-2026-codewiki">
<titleInfo>
<title>CodeWiki: Evaluating AI’s Ability to Generate Holistic Documentation for Large-Scale Codebases</title>
</titleInfo>
<name type="personal">
<namePart type="given">Anh</namePart>
<namePart type="given">Nguyen</namePart>
<namePart type="family">Hoang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Minh</namePart>
<namePart type="family">Le-Anh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bach</namePart>
<namePart type="family">Le</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nghi</namePart>
<namePart type="given">D</namePart>
<namePart type="given">Q</namePart>
<namePart type="family">Bui</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<abstract>Comprehensive software documentation is crucial yet costly to produce. Despite recent advances in large language models (LLMs), generating holistic, architecture-aware documentation at the repository level remains challenging due to complex and evolving codebases that exceed LLM context limits. Existing automated methods struggle to capture rich semantic dependencies and architectural structure. We present CodeWiki, a unified framework for automated repository-level documentation across seven mainstream programming languages. CodeWiki combines top-down hierarchical decomposition with a divide-and-conquer agent system to preserve architectural context and scale documentation generation, and a bottom-up synthesis that integrates textual descriptions with visual artifacts such as architecture and data-flow diagrams. We also introduce CodeWikiBench, a benchmark with hierarchical rubrics and LLM-based evaluation protocols. Experiments show that CodeWiki achieves a 68.79% quality score with proprietary models, outperforming the closed-source DeepWiki baseline by 4.73%, with especially strong gains on scripting languages. CodeWiki is released as open source to support future research.</abstract>
<identifier type="citekey">hoang-etal-2026-codewiki</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.288/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>5812</start>
<end>5827</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T CodeWiki: Evaluating AI’s Ability to Generate Holistic Documentation for Large-Scale Codebases
%A Hoang, Anh Nguyen
%A Le-Anh, Minh
%A Le, Bach
%A Bui, Nghi D. Q.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F hoang-etal-2026-codewiki
%X Comprehensive software documentation is crucial yet costly to produce. Despite recent advances in large language models (LLMs), generating holistic, architecture-aware documentation at the repository level remains challenging due to complex and evolving codebases that exceed LLM context limits. Existing automated methods struggle to capture rich semantic dependencies and architectural structure. We present CodeWiki, a unified framework for automated repository-level documentation across seven mainstream programming languages. CodeWiki combines top-down hierarchical decomposition with a divide-and-conquer agent system to preserve architectural context and scale documentation generation, and a bottom-up synthesis that integrates textual descriptions with visual artifacts such as architecture and data-flow diagrams. We also introduce CodeWikiBench, a benchmark with hierarchical rubrics and LLM-based evaluation protocols. Experiments show that CodeWiki achieves a 68.79% quality score with proprietary models, outperforming the closed-source DeepWiki baseline by 4.73%, with especially strong gains on scripting languages. CodeWiki is released as open source to support future research.
%U https://aclanthology.org/2026.findings-acl.288/
%P 5812-5827
Markdown (Informal)
[CodeWiki: Evaluating AI’s Ability to Generate Holistic Documentation for Large-Scale Codebases](https://aclanthology.org/2026.findings-acl.288/) (Hoang et al., Findings 2026)
ACL