How to Reverse Engineer Specifications from Code with A Coding Agent

How to Reverse Engineer Specifications from Code with GitAuto

In many development environments, documentation often becomes outdated as code evolves. Engineers typically dislike maintaining documentation, and the mantra "the code is the documentation" only works until your codebase grows beyond a certain size or your team expands. Let's explore how GitAuto can help solve this common problem.

TL;DR - What's the story?

1. The Documentation Challenge
2. Testing GitAuto's Reverse Engineering Capabilities
3. Standardizing Documentation Output
4. Scaling with GitHub Issues
5. Limitations and Considerations
6. The Bigger Picture

1. The Documentation Challenge

Let me share two real-world scenarios that highlight the documentation dilemma:

Scenario 1: The Waterfall Project

A client working in a waterfall-like IT service environment requires formal documentation deliverables, including basic and detailed design specifications. However, in their actual development process, these documents frequently become outdated as implementation progresses. Eventually, reverse engineering the specifications from the code becomes more efficient than maintaining the original documents. Most engineers dislike documentation maintenance tasks, which often fall to business-development hybrid roles.

Scenario 2: The Growing SaaS Product

A SaaS development team has seen their codebase grow to millions of lines of code, with over 100 developers working across multiple programming languages. With increasing team turnover and new members joining, the "the code is the documentation" approach no longer suffices. There's growing pressure to reverse engineer specifications from the code to improve comprehension without requiring deep code reading, ultimately aiming to enhance development speed and simplify maintenance.

Scenario 3: The API Provider

For companies that expose APIs to external developers and publish API documentation online, the documentation challenge is even more critical. In these cases, outdated or inaccurate documentation doesn't just slow down internal development—it directly impacts customer experience, developer adoption rates, and ultimately revenue. When external developers encounter discrepancies between documentation and actual API behavior, they lose trust in the platform and may abandon it altogether. These companies often dedicate significant resources to documentation maintenance, making automation particularly valuable.

Understanding these challenges, let's see if a coding agent can help.

2. Testing GitAuto's Reverse Engineering Capabilities

I decided to test whether GitAuto could effectively reverse engineer documentation from code. My first experiment focused on generating API documentation for a specific endpoint.

Here's the process I followed:

First, I created a GitHub issue requesting documentation generation, which says "Reverse-engineer an API specification in Markdown from app/api/auth/[...nextauth]/route.ts.":

GitAuto immediately appeared in the issue comments, ready to assist:

I checked the box to assign GitAuto to the task:

GitAuto then began scanning the necessary files in the repository:

Within 2-3 minutes, GitAuto completed the entire process and created a pull request:

The result was a well-structured API specification document in Markdown format:

The results were promising. GitAuto successfully extracted the endpoint's overview, authentication flow, endpoint details, methods, parameters, and additional information from the code. However, reviewing this output revealed two important insights for me:

1. I needed a standardized documentation template to ensure consistency across multiple documents, meaning I noticed I already had a specific format in mind
2. I should establish naming conventions and output directory structures for the generated documentation because the file name API_Spec_Auth.md and the document output directory . were not optimal for me

3. Standardizing Documentation Output

Based on my initial findings, I created a parent GitHub issue that defined documentation standards:

The template included sections for:

• Endpoint Overview
• Request Parameters
• Response Format
• Error Handling
• Authentication & Authorization
• Rate Limits (if applicable)
• Versioning (if applicable)
• Other best practices

I also specified output directory structure and file naming conventions:

This is called the explicit codification of knowledge - transforming tacit expertise into clear, documented processes that can be shared and followed by others. This approach is essential for scaling documentation practices across teams and ensuring consistent quality.

With these standards in place, I created sub-issues for individual API endpoints, each referencing the parent issue's template. The results were much more consistent:

Since GitHub renders Markdown diffs, reviewing these documents was straightforward. We also tested AsciiDoc format with similar success:

However, it's worth noting that unlike Markdown, AsciiDoc diffs don't render as rich views in GitHub's pull request interface (though they do render properly in the normal code view). This is an important consideration when choosing your documentation format.

4. Scaling with GitHub Issues

Once you established your documentation process, scaling became simple. Following our guide on opening pull requests from GitHub issues, you could:

1. Create multiple documentation issues (one per component/endpoint)
2. Apply the gitauto label to all issues at once
3. Let GitAuto process them in parallel

This approach allowed you to rapidly generate documentation for large sections of your codebase without diverting engineering resources from development tasks.

5. Limitations and Considerations

While this approach proved effective, it's important to acknowledge some limitations:

Format Constraints

GitAuto works with text-based formats like Markdown and AsciiDoc that integrate well with Git. It cannot directly generate Excel documents, which are still common in some industries. If your organization requires Excel-based documentation, you'll need an intermediate conversion step.

6. The Bigger Picture

Documentation maintenance is a persistent challenge in software development. By leveraging GitAuto to reverse engineer specifications from code, you can:

1. Keep documentation synchronized with implementation
2. Free up engineering resources for higher-value tasks
3. Improve onboarding for new team members
4. Enhance collaboration between technical and non-technical stakeholders

How are you managing documentation in your projects? Have you tried automated approaches? I'd love to hear your experiences and insights at [e-mail].