Cool project, I've been meaning to do this myself at work for a codebase, and it's nice to see that this exists now.
Does the project you simply compute embeddings for every function unit and cluster them, or do we also mean-pool significant dependencies of a function? In other words, given the function
def a():
b()
c()
d()
Do we also embed b, c, and d as well and combine them somehow in the embedding of a?
It looks like it works only on function bodies[1]. I'm not sure I understand why you would want to look at invoked callables code, though. Calling the same set of helper functions is already flagged; repeated code in helpers is flagged as well when those helpers are analyzed. Do you have a specific example where you'd like a function flagged as a duplicate based on the code it calls out to?
I built Slopo to solve one specific problem: finding similar code that is hardest to detect by other tools, coding AI agents, and humans.
It finds similar-looking code with embeddings. This detects more than just copy-paste clones or even clones with minor changes. Similar code is often not a clone to refactor, and this is a trade-off. Initial results need to be verified, but coding agents can do this quickly. Example prompts are available on https://slopo.dev
Additionally, similar code distant in the codebase is ranked higher to focus on less obvious duplication.
The results differ a lot depending on the codebase. I noticed that sometimes most of the detected duplicates are false positives, but the remaining ones are strong candidates to refactor or even bugs. Sometimes it reveals much more real duplication.
Correct me if I'm wrong, but looking at [1] it seems to be specifically using function definitions (I'm guessing this works with functions, methods, and lambdas (the "<unknown>" part)?) as units of repetition. If yes, that's fine, but I would seriously consider adding some settings to allow the user to control that granularity. Sometimes, the repeated code is a conditional branch within larger functions (i.e., "every else:" or "every except Ex:" looks the same). If the functions are large enough, the dissimilarity of the rest of the body would (probably?) cause such things to be missed.
I would also consider - perhaps as a separate pass, with scoring set differently - to analyze comments (especially docstrings in Python). If I read the code correctly, you're currently just stripping them, which is the right thing to do when looking for code duplication, but duplicated docstrings are also often a signal that something is wrong in the codebase. The "different scoring" is because we expect docstring to be structured similarly (at least more than normal code), so some tweaking would be needed.
I implemented this for a large monorepo last year, it runs as an analysis during code review and it shows what are possible similar snippets wrt the code under review. It was a very nice project. It also allows to see across the repo what are the most common constructs for the different languages. This could also be helpful to see if some code has been copied e.g. from open source projects.
No. It depends the most on general code quality and architecture. Some implementations require more code similarity by design. Some languages, like Java, may tend to have more duplication, but it's only a theoretical guess. It also depends on what kind of software is developed with what language.
There are good mature tools for deterministic duplication detection and I intentionally focused on embedding-based to fill this gap (I didn't find other tools using this approach).
If by "more efficient" you mean to avoid embedding of the same code multiple times, this optimization is already implemented internally.
This is a great use case for embeddings. Code deduplication across distant modules is notoriously hard for traditional AST-based tools.
How do you handle chunking and parsing for different languages to make sure the embeddings capture semantic meaning effectively? For instance, do you chunk by functions/classes, or use a fixed token window? If a function is too long or too short, it can drastically skew the embedding similarity.
Generally, I chunk by function/method (not by whole class), but different languages have specific concepts and features. Nested code units, anonymous functions, lambdas, closures are extracted as separate chunks.
The chunk size has allowed range and those outside are simply ignored.
- Upper limit is hardcoded with a body size of 10k chars
- Lower limit is configurable with a default of 10 AST nodes inside the body
The chunking strategy is something that can be improved in future versions.
Recently there was a popular article on HN saying that sometimes code duplication is better than abstraction, so I assume that this question is not a joke.
While testing this tool, one detected duplication was interesting for a use case. Permission check logic was duplicated and placed in different distant places in the codebase. The code was similar, but not identical, the logic was not the same. One version had stricter checks. I analyzed this with the coding agent, and we found out that both versions are used for the same thing, which means that in some cases validation is insufficient. Having only a single validation place, this bug could be prevented or easily detected.
I tend to follow the "rule of 3": a second similar implementation is OK, introducing the third triggers a refactor. As with everything, this isn't dogma, and sometimes the second implementation is already too much, while at other times you get tens of similar code sections (in codegen, repeating patterns with almost no changes is a virtue). But it's a good rule of thumb.
On testability: two implementations can be tested against each other, leading to greater coverage with less test code. It doesn't work that way for 3+ implementations, which is another reason not to have that many.
Does the project you simply compute embeddings for every function unit and cluster them, or do we also mean-pool significant dependencies of a function? In other words, given the function
Do we also embed b, c, and d as well and combine them somehow in the embedding of a?[1] https://github.com/rafal-qa/slopo/blob/main/src/slopo/indexi...
It finds similar-looking code with embeddings. This detects more than just copy-paste clones or even clones with minor changes. Similar code is often not a clone to refactor, and this is a trade-off. Initial results need to be verified, but coding agents can do this quickly. Example prompts are available on https://slopo.dev
Additionally, similar code distant in the codebase is ranked higher to focus on less obvious duplication.
The results differ a lot depending on the codebase. I noticed that sometimes most of the detected duplicates are false positives, but the remaining ones are strong candidates to refactor or even bugs. Sometimes it reveals much more real duplication.
I would also consider - perhaps as a separate pass, with scoring set differently - to analyze comments (especially docstrings in Python). If I read the code correctly, you're currently just stripping them, which is the right thing to do when looking for code duplication, but duplicated docstrings are also often a signal that something is wrong in the codebase. The "different scoring" is because we expect docstring to be structured similarly (at least more than normal code), so some tweaking would be needed.
Finally: very nice project, congrats! :)
[1] https://github.com/rafal-qa/slopo/blob/main/src/slopo/indexi...
If you are interested in data, you can check my article. Analysis was done with this tool, but a previous version where exact-copy duplicates were excluded from analysis. https://rkochanowski.com/article/analysis-code-duplication/
If by "more efficient" you mean to avoid embedding of the same code multiple times, this optimization is already implemented internally.
How do you handle chunking and parsing for different languages to make sure the embeddings capture semantic meaning effectively? For instance, do you chunk by functions/classes, or use a fixed token window? If a function is too long or too short, it can drastically skew the embedding similarity.
The chunk size has allowed range and those outside are simply ignored.
- Upper limit is hardcoded with a body size of 10k chars
- Lower limit is configurable with a default of 10 AST nodes inside the body
The chunking strategy is something that can be improved in future versions.
While testing this tool, one detected duplication was interesting for a use case. Permission check logic was duplicated and placed in different distant places in the codebase. The code was similar, but not identical, the logic was not the same. One version had stricter checks. I analyzed this with the coding agent, and we found out that both versions are used for the same thing, which means that in some cases validation is insufficient. Having only a single validation place, this bug could be prevented or easily detected.
If so - maintainability, testability. This is old software engineering best practice at this point.
You shouldn’t hyper optimize for deduplication, but it’s usually worth considering. Fewer places to fix issues or improve as well.
On testability: two implementations can be tested against each other, leading to greater coverage with less test code. It doesn't work that way for 3+ implementations, which is another reason not to have that many.