mizchi Python Code Similarity Detection
What to do
Run similarity-py on the target project to detect duplicate functions and classes, then analyze results and propose refactoring.
If similarity-py is not installed:
cargo install similarity-py
Step 1: Run similarity analysis
similarity-py $ARGUMENTS
If no arguments given:
similarity-py . --threshold 0.85 --min-lines 5
Step 2: Analyze results
High-priority
- 100% similarity: Identical functions with renamed variables -> extract shared function
- 95-100%: Same algorithm, different names -> parameterize
- Duplicate class methods: Same logic across classes -> extract mixin or base class
Medium-priority
- 85-95%: Similar data processing with minor differences -> shared utility
- Decorated variants: Same function with different decorators -> single function with configurable decoration
Acceptable
- Short
__init__methods that set attributes - Property getters/setters with trivial structure
Step 3: Propose refactoring
For each high-priority pair, show before/after code.
Key Options
| Option | Description |
|---|---|
--threshold <0-1> |
Similarity threshold (default: 0.85) |
--min-lines <n> |
Skip functions shorter than n lines (default: 3) |
--print |
Show actual code snippets |
--filter-function <name> |
Filter by function name |
--fail-on-duplicates |
Exit code 1 if duplicates found |
--experimental-overlap |
Enable partial overlap detection |
Common Python refactoring patterns
- Data processing functions with different field access -> generic with key parameter
- API endpoint handlers -> shared decorator or base handler
- Validation methods -> schema-based (pydantic, attrs)
- Duplicate class methods across classes -> mixin class or standalone function
- Test setup duplication -> fixtures or shared base test class