The biggest problem is not that bugs are migrated with COBOL, but that lots of new bugs are going to be introduced. AI is not deterministic, it will be making tons of mistakes. The only realistic low-error approach is incremental step-by-step migration using Cursor or similar tools. However, it requires much more time as each step must be prompted, tested and committed manually. Any hope that one-shot migraton of a large code base will not introduce enormous number of bugs is very naive. LLM is very bad on handling long context - it is their nature unfortunately. There is no answer to this problem yet.
300 to 4000 lines of "production like" (whatever that is) cobol code which is easily ported to a non-mainframe env. Our's sometimes uses assembler in its innards, so good luck with real legacy code spanning a dozen files and 50k loc...
I recently threw in (want to check those intelligence metrics!) some real production code into a non-agentic system (just to get a feel how things perform without a custom harness) and results where ... interesting. The particular program uses some preprocessor no LLM we have access to (newest was GPT 5.5) has any clue about - so they confabulate what it could do (Gemini 2.5 didn't even notice there was a preprocessor...). This is expected of course but it somehow seems the problem of this technology that unless you feed it masses of data or mechanically break up the tasks in rote subunits, it just doesn't do anything sensible still...
I remember a similar story shared this year at JAX2026 from the Sparkasse Group, they said they were using AI to migrate from COBOL, but they still were in the middle of the migration. Maybe they faced the same issues / problems? It seemed pretty zealous to me, that everything was working smoothly, but this article highlights the limitations
Anything running on COBOL to day is a large enterprise system. You'll have reports running in other systems in subsidiary companies that rely on bugs in the upstream cobol code.
Yeah. Bug-for-bug migration is a real thing in large code-bases in the industry. You want to replicate all behavior of the code regardless whether the behavior is a feature or a bug.
As specified, please find 99997 correct parts and the 3 defects (do not use)
There are hundreds of billions of lines of cobol in production.
The IRS alone has approx 160 cobol programs, averaging 230kloc each.
I recently threw in (want to check those intelligence metrics!) some real production code into a non-agentic system (just to get a feel how things perform without a custom harness) and results where ... interesting. The particular program uses some preprocessor no LLM we have access to (newest was GPT 5.5) has any clue about - so they confabulate what it could do (Gemini 2.5 didn't even notice there was a preprocessor...). This is expected of course but it somehow seems the problem of this technology that unless you feed it masses of data or mechanically break up the tasks in rote subunits, it just doesn't do anything sensible still...
COBOL isn't hard, the tooling around it on old systems are a pain though.
See Hyrum's Law: https://www.hyrumslaw.com/