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The problem is it'll convert 100% of the code base but (you hope) 50% of it will actually be correct. Which 50%? That's left as an exercise to the reader. There's no human, no plan, no logic necessarily to how it was converted also so it can be very difficult to understand code like that and you can't ask the person who wrote why stuff is a certain way.
Understanding large, complex codebases one didn't write is a difficult task even under pretty ideal conditions.
First, odds are only half the code is used, and in that half, 20% has bugs that the system design obscures. It’s that 20% that tends to take the lionshare of modernization effort.
It wasn’t a bug then, though it was there, but it is a bug now.
Please go read the article. They specifically say they aren't doing this.
I was speaking generally. In other words, the LLM will convert 100% of what you tell it to but only part of the result will be correct. That's the problem.
And in this case they're not doing that:
So you might feed it your COBOL code and find it only coverts 40%.
I'm afraid you're completely missing my point.
The system gives you a recommendation: that has a 50% chance of being correct.
Let's say the system recommends converting 40% of the code base.
The system converts 40% of the code base. 50% of the converted result is correct.
50% is a random number picked out of thin air. The point is that what you end up with has a good chance of being incorrect and all the problems I mentioned originally apply.
One would hope that IBM's selling a product that has a higher success rate than a coinflip, but the real question is long-term project cost. Given the example of a $700 million dollar project, how much does AI need to convert successfully before it pays for itself? If we end up with 20% of the original project successfully done by AI, that's massive savings.
The software's only going to get better, and in spite of how lucrative a COBOL career is, we don't exactly see a sharp increase in COBOL devs coming out of schools. We either start coming up with viable ways to move on from this language or we admit it's too essential to ever be forgotten and mandate every CompSci student learn it before graduating.
Again, my point really doesn't have anything to do with specific percentages. The point is that if some percentage of it is broken you aren't going to know exactly which parts. Sure, some problems might be obvious but some might be very rare edge cases.
If 99% of my program works, the remaining 1% might be enough to not only make the program useless but actively harmful.
Evaluating which parts are broken is also not easy. I mean, if there was already someone who understood the whole system intimately and was an expert then you wouldn't really need to rely on AI to port it.
Anyway, I'm not saying it's impossible, or necessary not going to be worth it. Just that it is not an easy thing to make successful as an overall benefit. Also, issues like "some 1 in 100,000 edge case didn't get handle successfully" are very hard to quantify since you don't really know about those problems in advance, they aren't apparent, the effects can be subtle and occur much later.
Kind of like burning petroleum. Free energy, sounds great! Just as long as you don't count all side effects of extracting, refining and burning it.
A random outcome isn't flipping a coin, it's rolling dice