this post was submitted on 20 Oct 2023
588 points (97.9% liked)
Programmer Humor
32503 readers
387 users here now
Post funny things about programming here! (Or just rant about your favourite programming language.)
Rules:
- Posts must be relevant to programming, programmers, or computer science.
- No NSFW content.
- Jokes must be in good taste. No hate speech, bigotry, etc.
founded 5 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
I'm convinced there must be a way of using ASTs to do more intelligent diffing of a given programming language, but I'm far too lazy to find out for myself.
Diffing algorithms on trees might not be as efficient, especially if they have to find arbitrary node moves.
I wouldn't expect it to be, but I think modern processors can handle the load!
It's not necessarily about the load, it's about the algorithmic complexity. Going from lists (lines in a file, characters in a line) to trees introduces a potentially exponential increase in complexity due to the number of ways the same list of elements can be organized into a tree.
Also, you're underestimating the amount of processing. It's not about pure CPU computations but RAM access or even I/O. Even existing non-semantic diff implementations are unexpectedly inadequate in terms of performance. You clearly haven't tried diffing multi-GB log files.
Log files wouldn't fall under the banner of compiled languages or ASTs, so I'm not sure how that example applies.
And I'm aware that it can lead to O(n²) complexity but, as others have provided, there are already tools that do this, so it is within the capabilities of modern processors
Yes there will be cases where the size of the search space will make it prohibitive to run in reasonable times but this is - by merit of the existing tools and the fact that they seem to work quite well - an edge case.
Log files themselves don't, but I'm just comparing it with simpler files with simpler structure with simpler algorithms with better complexity.