It's hard to counter assumptions. How would you answer "I think criminals were hugged much less by their parents when they were a child. Is there a flaw with this assumption?"
You can't specifically prove that the opposite of an assumption is inherently more likely. But that doesn't mean that your assumptions are therefore true.
Just as a quick mathematical exercise, if all your assumptions are individually 90% correct (which is heavily erring in your favor), then the odds of being correct on 5 assumptions are roughly 59%. At 7 assumptions, you'd be doing worse than a coin flip (48%).
All your assumptions can be countered
When was it last updated?
Assumption: older code needs more attention.
This is at odds with assumptions 2 and 3. The less something has been updated (assumption 1: bad sign), the less likely it is to have had multiple committers (assumption 3: good sign?) or to have had a lot of commits (assumption 2: good sign?).
To put it another way, if observed on a random day, a code file with many revisions is on average "fresher" (i.e. changed more recently) than a code file with fewer revisions. This pits your assumptions against one another.
Furthermore, I can very easily argue the opposite. The code that hasn't been touched in ages clearly didn't need to be touched, so it's arguable better than the code that was touched recently. Clean code leads to readable code, readable code leads to lowered chance of bugs/mistakes and higher chance of being able to implement a fix even if there is a bug. In either case, doing things well means they can be left undisturbed for a longer time.
How many revisions?
Assumption: too many revisions is a sign of unstable code.
Michael paints a picture in 10 minutes. Leo takes 30 days. Whose painting is the best?
Well, you could argue that Michael must be better because Leo is wasting 29 days 23 hours and 50 minutes compared to Michael, so Leo must be a worse painter, right? But then you'd be assuming that Michael and Leo were delivering a painting of equal detail and skill level. It's highly unlikely that this is the case, and even if it were, it defeats the purpose of calling one painting better than the other.
It's more likely that Michael's painting looks slapdash, and Leo's painting looks significantly more detailed and skillful due to the extra time taken.
Or, to drop the analogy, the additional time spent revising code is not more likely to have decreased its quality than to have improved it, as per your assumption. It can be argued either way, and it's much too contextual and volatile to make any blanket assumption about.
How many different developers worked on it?
Assumption: too many developers leads to inconsistent design.
The biggest dragons in legacy code are codebases built by a single person. Single developers work in their own personal style (as opposed to one with team consensus), tend not to document (since knowledge transfer is rarely if ever needed), and tend to learn to live with their own code's quirks (as opposed to people bumping into each other's quirks and fixing them).
I have years worth of experience to argue the opposite of your assumption.
How many lines of code?
Assumption: the more code the longer or more difficult the task is.
Clean code tends to take up more space (by line count, and often file count too), but is orders of magnitude easier to read, understand, refactor or change. That defeats your assumption and argues the exact opposite.
How many references to it exist?
Assumption: probability of bugs increases with the number of dependents.
My aunt lives on a little rural dead end street. Only her and her neighbor use the little road to their properties. It's a bumpy gravel road with potholes, and horrible to drive on.
I live in a city center, on a main road. The road is made with high quality whisper asphalt, and is a joy to drive on.
We both live in the same city. Same local government, same transportation budget, same staff fixing potholes. And yet my road has less potholes than my aunt's road, because more people use it.
A piece of code that has more references to it suggests that it is being used more, and you're much more likely for consumers to stumble on bugs or issues, and thus for more bugs or issues to be noticed and subsequently fixed.
Just to be clear, I am not saying you should assume the opposite of what you have been assuming. I'm trying to point out that all your assumptions are not guarantees, as I can very easily come up with scenarios where your assumptions simply do not hold.
Pudding, meet proof
You've made your assumptions, you've got your answer based on your assumptions. What's missing is confirming the validity of your theory.
Rather than ask some strangers on the internet who have zero contextual knowledge of your environment, monitor if the actual resolution time reasonably approximates the estimated effort calculated by your as of yet unproven theory.
No developer has ever been able to estimate work correctly, other than by trying to estimate, realizing they estimated inaccurately, and then learning to counterbalance their inaccuracy. With each new project/environment/company, that learning curve somewhat resets, as certain environmental factors will have changed.
Your calculation is no different. It's probably very imprecise, but could possibly be tweaked by monitoring and adjusting it to your specific environmental factors.
For example, if Bob revised the same file 4 sprints in a row, that may be indicative of shoddy code. I've known a few Bobs like that.
This is not something a stranger on the internet can tell you, as there are too many contextual factors at play. You simply need to put in the effort to test your assumptions and finetune them based on your findings.