* Single-dimensional. Diffs of text lines are just this.
* Multi-dimensional. Diffs of words or characters are usually going to be this since lines still matter, but there are multiple approaches (line-first? weighted tokens?).
* Tree-based. Unfortunately, these are woefully scarce and poorly documented.
For text diffs, it's nontrivial to get the "missing newline at end of file" logic working.
For tree diffs, consider that for HTML something like `<p>x</p><p>y</p>` should be unmergeable, whereas `<b>x</b><b>y</b>` should be mergeable.
(Aside: the blind promotion of `<em>` and `<strong>` did great harm to the notion of semantic HTML. Most things people use italics for (book titles, thoughts, foreign words) are explicitly things that `<em>` should not be used for.)
E.g., in the example scenario of the diff in json objects, if a possible operation is a change in a property value (such as the "id" field), then the diff correctly deduced the smallest change possible is indeed a change in the field.
However, if you can define the set of operation to only be a change in an entire object (and no changing of id field), then surely, you can create a diff that produces the desired object structure change. It would be a custom diff algorithm of course...but it'd be quite a useful one tbh.
You can always represent a change as a removal and an addition. It’s smart to actually consider when should you. “Never” and “whenever possible” don’t seem like the best answers.
One paragraph.
Followed by another.
However if the html is "an application" more than it is "a document" - a b-tag with two letters, might be meaningfully different from two b-tags in sequence (for example with css:)
b { display: block }
Ed: ie if diffing input from a html input field (rich editor) merging bold tags would probably be what you want - when the first edit bolds first letter, and second edit bolds second letter.
MDN talks about when a specific element is appropriate, but it doesn’t really help you discover those elements that might be relevant.
I'd much rather have a slower, but more accurate searcher, or one that was easier to use as an API.
When I left the field, the latest hottest thing was diamond (https://github.com/bbuchfink/diamond).
https://diff2html.xyz/ -- https://github.com/rtfpessoa/diff2html
> they are sometimes read differently by people from different language backgrounds
I'm really curious of some examples you have actually how an image is influenced by language ?
There's decent empirical evidence to show that peoples' language influences their perception of colour:
Language and Color Perception: Evidence From Mongolian and Chinese Speakers https://pmc.ncbi.nlm.nih.gov/articles/PMC6426779/
Tom Scott, All The Colours, Including Grue: How Languages See Colours Differently https://www.youtube.com/watch?v=2TtnD4jmCDQ
Similar effects for perception of time.
It's very plausible that emojis are perceived differently too, although I'm not aware of any studies on it at the moment.
Naturally it's quite hard to talk about, because people don't necessarily have the words to describe the differences between what they experience, or are even aware of a difference in the first place.
PS regarding readability, I think VSCode put a lot of effort into creating nice-to-read diffs (e.g. https://code.visualstudio.com/updates/v1_81#_diff-editor), some of which is done in the algorithm itself (https://code.visualstudio.com/updates/v1_78#_diff-algorithm-...). But apparently that's in TypeScript, and not all heuristics done there for an editor is suitable to be in a generic diff algorithm. Still, there might be something worth exploring.
Long story short, due to the conservative culture, most data structures and algorithms were implemented in house. The diff algorithm for packets/segments/payloads was written in house too and I was the one to write it.
My implementation was based on a straightforward dynamic programming solution to the longest common subsequence problem. If I recall correctly, it ran in O(mn) time and O(min(m, n)) space in the worst case, where m and n are the lengths of the two sequences. I knew there were more efficient algorithms, but this code was not performance critical. I chose to keep the implementation simple so anyone could understand it, learn it quickly, and fix bugs if they arose. It served us well for the next seven years until the product was replaced with a new one.
On a related note, I sometimes miss that older style of software development where we would dive deep into a problem domain, master it, and design solutions ourselves. I am not being naively nostalgic though. I am very well aware that modern development, with its reliance on well established libraries, usually delivers much greater productivity and reliability. Still, I think the slower and more deliberate approach of building things from the ground up had a certain charm.
They all seem to be names for more or less the same idea.
The first time a test runs successfully it auto captures the output as a file. This is the "approved" output and is committed with the code or saved in whatever test system you use.
The next time the test runs, it captures the new output and auto compares it with the approved output. If identical, the test passes. If different, the test fails and a human should investigate the diff.
The technique works with many types of data:
* Plain text.
* Images of UI components / rendered web pages. This can check that your code change or a new browser version do not unexpectedly change the appearance.
* Audio files created by audio processing code.
* Large text logs from code that has no other tests. This can help when refactoring, hopefully an accidental side effect will appear as an unexpected diff.
See: * https://approvaltests.com/ * https://cucumber.io/blog/podcast/approval-testing/ * https://en.wikipedia.org/wiki/Characterization_test
Also in the legal space, sorting through discovery can be incredibly tedious. There are lots of diff-based and diff-like solutions in this space; most are completely proprietary and undocumented.
I also used diff at work today to compare the output of two different 'docker history' outputs to look for what a high-level overview of changes made by a contractor tasked with hardening a base image.
- integrity checks from security perspective
- nlp, finding same tokens in text
Etc
curses had the task of updating your 2400 baud terminal screen to look like a TUI program's memory image of what should be on it. (The TUI might be vi, nethack, a menu system, a database entry screen, ntalk, whatever.) The simple solution is to repaint the whole screen. But 80x24 is 2000 bytes, which is 8 seconds at 2400 baud. Users won't use a program that takes 8 seconds to respond after their latest keystroke. So curses uses a diff algorithm and the terminal capabilities database to figure out a minimal set of updates to send over the serial line. (Some terminals have an escape sequence to insert or delete a line or a character, which can save a lot of data transmission, but others don't.) React's virtual DOM is the same idea.
If you run `du -k > tmp.du` you can later `du -k | diff -u tmp.du -` to see which directories have changed in size. This can be very useful when something is rapidly filling up your disk and you need to stop it, but you don't know what it is.
If you `sha1sum $(find -type f) > good.shas` you can later use diff in the same way to see which files, if any, have changed.
rsync uses rolling hashes to compute diffs between two computers at opposite ends of a slow or expensive connection in order to efficiently bring an out-of-date file up-to-date without transmitting too much data. It's like curses, but for files, and not limited by terminal capabilities.
rdiff uses the rsync algorithm to efficiently store multiple versions of a file, which does not in general have to contain source code. Deduplicating filesystems can use this kind of approach, but often they don't. But it's common in data backup systems like Restic.
It's common for unit tests to say that the result of some expression should be some large data structure, and sometimes they fail by producing some slightly different large data structure. diff algorithms are very valuable in understanding why the test failed. py.test does this by default.
Genomic analysis works by finding which parts of two versions of a genome are the same and which parts are different; BLAST, by Gene Myers, is a common algorithm for this. This is useful for an enormous variety of things, such as understanding the evolutionary history of species, identifying cancer-causing mutations in DNA from tumors, or discovering who your great-grandmother cheated on her husband with. It's maybe reasonable to say that all of bioinformatics is real-world use-cases of diff algorithms. They have to handle difficult pathological cases like transposition and long repeated sequences.
Video compression like H.264 works to a great extent by "motion estimation", where the compressor figures out which part of the previous frame is most similar to the current one and only encodes the differences. This is a more difficult generalization of the one-dimensional source-code-diff problem because while the pixel values are moving across the image they also can get brighter, dimmer, blurrier, or sharper, or rotate. Also, it's in two dimensions. This is pretty similar to the curses problem in a way: you want to minimize the bandwidth required to update a screen image by sending deltas from the image currently on the screen. Xpra actually works this way.
I noticed that some peoble have worked on such an algorithm, e.g. https://en.wikipedia.org/wiki/User:Cacycle/diff
I am currently researching and have built my own naive implementation for diffing a dense tree and the performance... isn't very great.
The ideal outcome is to create patches by diffing different versions of the JSON object, then being able to apply said patches anywhere in the "commit" tree.
https://github.com/Wilfred/difftastic
No really, if you haven't tried it, it's better than you think it is.
https://www.scannedinavian.com/tools-built-on-tree-sitters-c...
(I know, already mentioned later in comments by leeoniya, still deserves a top level comment!)
For example, a diff like:
+ x += 1
- x -= 1
https://technotales.wordpress.com/2009/05/17/git-diff-with-v...
Nugroho (2019): How Different Are Different diff Algorithms in Git?