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Related (but focused on the stock aspect of it all): #1440907

COBOL is everywhere. It handles an estimated 95% of ATM transactions in the US. Hundreds of billions of lines of COBOL run in production every day, powering critical systems in finance, airlines, and government.

I'd be interested in getting similar numbers on Fortran (or cpp) in the context of legacy physics code...

Despite that, the number of people who understand it shrinks every year.
The developers who built these systems retired years ago, and the institutional knowledge they carried left with them. Production code has been modified repeatedly over decades, but the documentation hasn't kept up. Meanwhile, we aren't exactly minting replacements—COBOL is taught at only a handful of universities, and finding engineers who can read it gets harder every quarter.

Yeah, I was the only one willing to use Fortran in the group I worked at previously. Young people don't like those old codes. I'm happy I've joined a group now where the number of Fortran devs is a bit higher, still.

COBOL modernization differs fundamentally from typical legacy code refactoring. You aren’t just updating familiar code to use better patterns, you’re reverse engineering business logic from systems built when Nixon was president. You’re untangling dependencies that evolved over decades, and translating institutional knowledge that now exists only in the code itself.

Rhaaa, I find #AISlop patterns everywhre now.

Modernizing a COBOL system once required armies of consultants spending years mapping workflows. This resulted in large timelines and high costs that few were willing to take on.
AI changes this.
These tools can:
  • Map dependencies across thousands of lines of code
  • Document workflows that nobody remembers
  • Identify risks that would take human analysts months to surface
  • Provide teams with the deep insights they need to make informed decisions
With AI, teams can modernize their COBOL codebase in quarters instead of years.

I'll let you read the rest of the article if you're interested in more specifics.

https://resources.anthropic.com/code-modernization-playbook

I'd be happy to find a similar playbook for my old legacy FORTRAN code. Not to rewrite it in a different language, but to get rid of all the spaghetti-code...

Even without playbook, if there is one thing LLMs are good at, it's refactoring code. I'm just too chicken to let it do it more aggressively without my oversight...

117 sats \ 0 replies \ @zuspotirko 1h

As someone who has spend a lot of his working life refactoring old code from old languages:

  • This is approach is useless. 90% of the value in refactoring old code is taking responsibilty and being liable if shit goes wrong. Is Anthropic liable for translated code? No? Then you can only help me with the remaining 10% of the challenge.
  • "Modernizing a COBOL system once required armies of consultants spending years mapping workflows". Yes. And what these people did/do is not translating the code into a new programming language. What these people do is understanding what the system is supposed to be doing such that they can verify the results.

Conclusio: A chatbot with a chat window already provided most of the value of the product. The agent working on it might even be a downgrade compared to the chat window.

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Even if you do oversight, it will still make the refactoring work a lot quicker

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