Show HN: CLI tool for detecting non-exact code duplication with embedding models
github.com75 points by rkochanowski 10 hours ago
75 points by rkochanowski 10 hours ago
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.
Finally: very nice project, congrats! :)
[1] https://github.com/rafal-qa/slopo/blob/main/src/slopo/indexi...
Currently, only whole functions (including function-like constructs depending on language) are considered as unit.
Skipping the extraction of conditional branches was my decision to not overcomplicate the first versions, which was intended to validate the idea. I will add this in future versions because I agree it's needed for large functions.
I don't think it needs configurable granularity. In the current version, there is an analogous mechanism: when functions are nested, both outer and inner are embedded separately. When both are similar to each other, this pair is excluded. Inner or outer functions can appear in results depending on similarity to other units.
Regarding comments, they are removed and I will think about handling them. The challenge is not with extraction, but with how to present this in a report. This may be a nice addition because coding agents often add comments.
Thanks for the feedback.
What a clever little tool. This is exactly the kind of pragmatic AI tools I want to see more of: linux-y single purpose tools!
If it did PHP I would love to run it over WordPress. What would it take to add that?
The false positive rate you're describing matches what we see running similarity detection on generated text instead of code: cosine similarity alone flags a lot of same-topic pairs that aren't actually duplicates. What helped was combining the embedding score with a structural signal (AST edit distance for code, overlapping headings and citations for text) so no single metric makes the call. Also worth surfacing the raw similarity score in the CLI output instead of just a binary duplicate flag, since people will want to tune the threshold per codebase.
My solution for false positives is simpler:
1. The tool uses only cosine similarity plus boost depending on distance in the codebase.
2. Classification with LLM. This can be done by coding agent used with project giving better results than integrating this pass in the tool. LLMs used for coding are pretty good.
I assumed that this is not a problem I need to solve inside the tool. I'm aware this is not deterministic, but this is by design.
Regarding information about raw similarity: currently, the score (raw similarity + boost) is visible in the report, so this value can be configured based on data. The raw similarity threshold can also be configured, but it's not displayed. I will think about how to handle this.
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()
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?
[1] https://github.com/rafal-qa/slopo/blob/main/src/slopo/indexi...
Based on your example there is only a single function a() which is embedded. The rest is just a code and dependencies are not resolved. Did you think about adding this feature in your tool?
Looks very cool! I'd be very interested in applying this to my Elixir projects. What does it take to add proper support for a new language?
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.
Nice, what's the chunking level? I would want sub function, logical blocks, etc
Nice idea. I can see this being useful before refactors, especially when the duplication is semantic rather than copy paste.
This is neat. Have you noticed any difference in duplicate detection between strongly typed and loosely typed languages / code bases?
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.
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/
Did you benchmark it against simpler methods like BM25?
I just focused on embeddings without comparing them to deterministic solutions.
But I plan to do my own analysis of different embedding models in the context of code similarity detection. Including BM25 in the comparison is a very good idea.
What a simple and smart idea. Wonderful
Very nice. I can imagine putting this into a pre push hook to keep things clean after an initial sweep.
looks super useful- thanks for sharing!
have you considered a deterministic tier before the embedding pass? I feel that approach can be more efficient.
We did this by using the ASTs you can go quite far without embeddings and the result is easier to debug and follow what's going on.
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.
[flagged]
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.
I think that this is pretty cool, but is there any reason why we would want to remove similar/possible duplicate code?
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.
(without sarcasm) Is this a serious question?
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.
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.