AI code documentation: Why documentation hurts and how AI helps

What is AI code documentation? 

AI code documentation involves the use of artificial intelligence technologies to create, manage, and update documentation for software development projects. This approach uses AI to understand codebases and generate human-readable explanations and documentation that accompany software code. 

The primary aim is to automate the documentation process, reducing the time and effort traditionally required. This can improve the accessibility and comprehension of complex software, facilitating easier maintenance and development and improving the user experience.

By employing advanced technologies like large language models (LLMs), AI can analyze vast amounts of code and technical content, learn their patterns, and use them to generate relevant documentation or comments for specific user-provided code. This innovation is having a major impact on how developers document, maintain, and communicate around their codebase.

What are AI documentation tools?

Modern AI documentation tools rely on machine learning algorithms, most notably large language models (LLMs). These models have been trained on a broad spectrum of code and documentation, enabling them to understand and produce human-like text that explains code functionalities, usage, and architecture. 

This ability to automatically generate documentation, comments, and code examples makes documentation more efficient and less prone to human error. The emergence of AI documentation tools signifies a shift from traditional, manually intensive documentation practices to a more dynamic, automated, and integrated approach. 

These tools can generate documentation from scratch, create specific documentation elements in response to natural language prompts, and update existing documentation to reflect code changes. This significantly reduces the occurrence of documentation drift and ensures that the code and its accompanying documentation evolve together.

Why documentation hurts and how AI can help 

There are several challenges involved in documenting code using traditional means.

1. Documentation drift

Documentation drift refers to the divergence between software documentation and the current state of the software itself. This occurs when updates to the software are not accurately or timely reflected in the documentation. The consequence is outdated or irrelevant information that can mislead developers, impede onboarding, and complicate maintenance efforts.

To mitigate documentation drift, continuous synchronization between code changes and their corresponding documentation updates is necessary. However, this requires significant effort and diligence, often leading to documentation being neglected or deprioritized in fast-paced development environments.

You can mitigate documentation drift by using AI documentation tools to quickly update and revise documentation as code changes occur. The AI can automatically review large volumes of documentation, and you can ask it whether existing documentation matches the current codebase. Using AI tools will facilitate quick reviews and audits of documentation to ensure ongoing relevance.

2. Lack of consistency

Consistency in documentation style, terminology, and format is essential for clear and effective communication. Unfortunately, maintaining consistency is challenging, especially in large projects or teams with multiple contributors. Variations in document structure, naming conventions, and level of detail can confuse readers and degrade documentation quality.

Standardizing documentation practices can help address this issue. However, achieving uniformity across all documents remains a substantial challenge in traditional documentation processes.

Using AI documentation tools will enforce consistent terminology and style across all documentation automatically. You can also train AI tools to your specific requirements for example if you want the tool to write to templates to standardize documentation format across multiple projects. 

3. Version control challenges

Version control is vital for tracking and managing changes in software projects, including documentation. However, integrating documentation into version control workflows is often overlooked. This omission can lead to discrepancies between code versions and their associated documents, complicating rollback procedures and historical reviews.

Implementing version control for documentation alongside code requires careful planning and adherence to best practices. Despite its importance, many teams struggle with effectively integrating documentation into their version control strategies. Documentation as code is a development paradigm that is helping address this challenge.

Keep documentation up-to-date as versions change with AI. For teams integrating documentation with version control, AI documentation can help by generating version-specific documentation at the click of a button when code is updated. AI tools can also link documentation directly to code commits for easy reference and generate summaries of pull requests to help document code changes.

4. Limited context

Traditional documentation often lacks the context necessary for comprehensive understanding. Documents might not adequately explain the reasoning behind design decisions, dependencies, or potential impacts of changes. This issue is exacerbated in complex projects where understanding the broader implications of code is crucial.

Enhancing documentation with contextual information requires diligence and foresight. Developers must commit to not only documenting what the code does but also why it was written that way, which demands additional time and effort.

Not every AI code assistant out there considers the context in your code. If you’re going to use AI, select a tool that provides enriched context by analyzing and documenting the broader impact of code changes. AI tools will build upon themselves. As you use them to generate documentation for complex code blocks explaining their purpose and integration, the AI will take that documentation and code into account for subsequent generations. 

Some AI tools can also gain context from internal documents, such as product requirement documents or design documents, to add background to technical documentation. And training your AI on your codebase can enhance understanding by documenting use cases and scenarios affected by the code.

5. Resistance to documentation

Many developers view documentation as a tedious, time-consuming task that diverts from their primary goal of writing code. This resistance stems from the perception that documentation offers little immediate benefit and can be outdated by the time it’s completed. As a result, documentation is often neglected or treated as an afterthought.

Overcoming this resistance requires a cultural shift within the development team. Emphasizing the long-term benefits of documentation and integrating it into the development process as a standard practice can help mitigate this challenge.

Don’t love writing documentation? Most developers don’t. Using AI reduces manual effort by automating routine documentation tasks. You can generate initial drafts of documentation, making the process faster and less burdensome. Adopting AI tools will encourage documentation culture by demonstrating the immediate utility of up-to-date documents.

Related content: Read our guide to AI for software testing 

How Tabnine’s generative AI streamlines code documentation 

Here is how Tabnine’s modern AI code assistant can be used for code documentation.

Automated comment generation

Automated comment generation leverages AI to produce explanatory comments directly within the code, providing instant clarification for complex functions and algorithms. This not only enhances the readability of the code but also supports maintenance and debugging efforts. It also becomes much easier to update code comments as the code evolves, minimizing documentation drift.

Additionally, automated comment generation can adapt to the coding standards and comment styles specific to a project or team. This ability to customize output makes it a valuable tool for maintaining consistency across a project’s documentation, helping new developers understand code quickly. 

To generate comments with Tabnine, just highlight or call out the code you want to be documented and ask Tabnine to generate inline, comment, or other forms of documentation through the chat window — all without leaving your IDE. 

Documentation aligned to your context, codebase, and standards

AI documentation tools greatly simplify the application of documentation standards. Tabnine maintains a consistent approach to documentation by considering the context and existing documentation in your IDE, workspace, and codebase. 

Additionally, you can provide Tabnine with expert guidance and fine-tune the underlying model, personalizing its documentation generation to your company and code. Tabnine’s personalization capabilities ensure each piece of documentation is consistent and contextually relevant.

Code explanation

Tabnine can automatically generate concise summaries of code blocks, entire modules, or even entire codebases. This feature is particularly useful in complex software projects. Summaries provide a high-level overview of what the code does, helping developers quickly grasp its purpose without delving into the details. This can significantly speed up the process of code reviews and onboarding new team members.

Tabnine analyzes the structure and logic of the code to highlight key functionalities and outcomes, presenting them in a clear, understandable manner. The summarization not only aids in documentation but also supports developers in identifying redundant or inefficient code, supporting maintenance efforts.

Code generation from documentation

Tabnine draws context from existing documentation in your codebase and from requirements platforms like Jira to generate code. It considers functions, classes, and APIs within a project to generate code tailored to the context of the codebase.

The code Tabnine creates is based on a deep understanding of the code’s intent and functionality, gathered through extensive analysis of the codebase by the AI. 

Code refactoring suggestions

Tabnine can perform code refactoring, suggesting improvements that enhance code quality and maintainability. These AI-driven suggestions focus on reducing complexity, improving readability, and optimizing performance. This not only contributes to cleaner code but also educates developers on better coding practices, promoting continuous improvement.

Leverage AI documentation in the enterprise with Tabnine

Using Tabnine’s AI coding assistant, you can effortlessly generate formal documentation for classes, functions, comments, and inline documentation. Tabnine helps development teams of all sizes streamline software development processes without compromising privacy, security, or compliance. Moreover, Tabnine’s AI code assistant understands your workflow and applications by recognizing context-aware projects, requirements, and codebases.

Get a free 90-day trial of Tabnine Pro today