DocSync: An Automated Code Documentation Maintenance System Based on Agent Feedback Loop

This article introduces the DocSync system, which combines an agent workflow with AST structure awareness and RAG retrieval enhancement to achieve automated code documentation maintenance and semantic consistency guarantee via the Critic-Guided Reflexion mechanism. Addressing the issue of documentation technical debt, the system uses a small model fine-tuned with LoRA, enabling high-quality documentation maintenance even in resource-constrained scenarios, with experimental performance significantly outperforming traditional baseline models.

In software development, maintaining documentation in sync with code is challenging. The evolution of codebases causes documentation to deviate from logic, forming "documentation technical debt" that reduces maintainability and leads to API misuse. Traditional static analysis tools can only detect the existence of documentation but cannot evaluate semantic consistency; LLM-generated documentation is prone to hallucinations and inconsistent with the actual behavior of code.

DocSync defines documentation maintenance as a "structured anchoring iterative generation task". It integrates AST parsing to obtain structured code representations and RAG retrieval for dependent contexts, building a reliable bridge between code syntax changes and natural language descriptions, allowing the model to understand relationships between code elements rather than isolated fragments.

The innovation of DocSync lies in the Critic-Guided Reflexion mechanism: after generating initial documentation update suggestions, the critic module evaluates factual consistency with the source code; if inconsistencies are detected, a correction loop is triggered for iterative self-improvement, enhancing documentation reliability.

The team focuses on resource-constrained scenarios, using small language models fine-tuned with LoRA instead of commercial large models; experimental setup uses proxy tasks (code-to-text maintenance) to simulate real-world scenarios, enabling controlled evaluation of different methods' effectiveness.

In automatic evaluation, DocSync achieved a score of 3.44/5.0, while the traditional CodeT5-base baseline only scored 1.91, with obvious gaps in dimensions like semantic alignment and summary line fidelity; the critic loop improved semantic correctness without increasing model parameters.

DocSync opens a new direction for automated documentation maintenance, proving the potential of combining structured retrieval and agent refinement; enterprise teams can deploy tools for automatic synchronization of documentation and code in the future, significantly reducing technical debt accumulation.

DocSync embodies AI system design trends: shifting from single-generation to iterative agent workflows, and from pure text understanding to deep code structure awareness; these concepts are expected to be applied and verified in more software development automation scenarios.