AI Generated Documentation for Legacy Software Systems

When a business keeps a product alive for ten or twenty years, the codebase becomes a time capsule of every “quick fix” and forgotten constraint it ever carried.

The people who wrote it have long since moved on, the original specifications are lost, and the only guide left is a maze of comments that contradict each other... or perhaps William upstairs still remembers how that one module works.

If companies need to retroactively document large, complex systems, the only option used to be code archaeology, and it went something like this:

1. Documentation triage – What docs do we have, where are they, how old are they, how correct are they, should we throw them out?
2. Audit meetings – Get all stakeholders together and try to map out what the system should do (does anyone still remember?).
3. Manual code reading – Thousands of source files sifted line by line: what does this do, where does it lead, is it even used any more?

Ask any analyst who has done it and they’ll tell you it feels like translating a foreign language with half the dictionary missing. Manually documenting a large legacy application can swallow months of effort and, even then, whole swathes of behaviour slip through the cracks.

You already have a source of truth

Buried beneath the cruft, the source code itself is the most reliable record you have. Every feature, every edge-case workaround, every hidden dependency is right there in plain text.

The catch? It’s often written in a language nobody on the current team speaks, peppered with deprecated functions, and tangled into a spiderweb of cross-references that make a simple “why does this button exist?” feel like detective work.

Even if you do have software engineers who can read the code, the rest of your business can't.

Teams don't need technical documentation that talks in files and classes - they need functional documentation that talks in features and flows.

Code is a great way for humans to speak to computers, but it's not a great way for computers to speak to humans — Chris Rickard, CEO of Userdoc

Automated documentation in the age of AI

LLMs can read code like it's English. They untangle call graphs, recognise patterns and, crucially, translate low-level operations into functional behaviour that humans can understand. What once took months of whiteboards and late-night coffee now happens while you grab lunch.

Userdoc turns that raw AI capability into something usable. Drop in your repository and, instead of abstract class diagrams, you get clear, human-centred artefacts – user stories, acceptance criteria and test scenarios – each one directly linked back to the lines of code that spawned it. The result is living, functional documentation that speaks to analysts, developers and executives alike.

This is what creating documentation for your legacy system with Userdoc looks like:

1. Code connect - Connect your source code repository (or securely upload a zip file)
2. Human in the loop - Confirm the AI-generated themes, and high-level features that are returned
3. Done - Get detailed requirements documentation in just hours, not months

Watch: AI reverse-engineers legacy code into documentation

See Userdoc's Code to Documentation in action i nthe below video.

Why it matters

Whether you’re planning a digital transformation, rebuilding a mission-critical platform or simply trying to understand the partner system you just inherited, fast, reliable documentation is the difference between confident change and risky guesswork.

With AI-generated docs, teams step into legacy codebases with clarity, not trepidation, and modernisation projects start moving at the speed the business expects.

Frequently Asked Questions

Question	Answer
Can AI document legacy languages like VB6, Delphi or COBOL?	Yes. If the source is plain-text in the repo or zip, Userdoc’s importer can parse it and generate functional docs regardless of age or language.
Is AI-generated documentation secure enough for regulated industries?	All processing runs in isolated containers, archives are deleted after import, and no customer code is used to train public models. You control who can view the output.
How accurate is Userdoc compared with a manual audit?	Internal benchmarks show 80 – 85 percent coverage of user-visible behaviour, achieved in a fraction of the time. Analysts can review and refine anything the AI misses.
What happens when we change the code later on?	Re-import the updated repo at any time. Userdoc highlights new, changed or removed features so the docs stay in sync.
Will the AI expose proprietary business logic to the wrong staff?	No. Documentation lives inside Userdoc workspaces with role-based access, so only authorised users can see sensitive features or rules.
Can it document stored procedures, triggers and user-defined functions in the database?	Absolutely. Include the SQL files (or a schema dump) in your upload and Userdoc treats them like any other source, generating docs for their behaviour.
We keep business rules in DB lookup tables—can Userdoc handle data-driven logic?	Yes, but it’s a bespoke setup. We’ll have a quick chat to map those tables to functional features so the AI interprets them correctly.