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The optimized code achieves a **3437% speedup** by implementing several micro-optimizations in the AST traversal logic within `_fast_generic_visit`:
**Key Optimizations Applied:**
1. **Local Variable Caching**: Stores frequently accessed attributes (`node._fields`, `getattr`, `self.__class__.__dict__`) in local variables to avoid repeated attribute lookups during traversal.
2. **Type Checking Optimization**: Replaces `isinstance(value, list)` and `isinstance(item, ast.AST)` with `type(value) is list` and `type(item) is ast.AST`. This avoids subclass checking overhead, providing ~7-12% performance gains for AST processing.
3. **Method Resolution Optimization**: Uses `self.__class__.__dict__.get()` to look up `visit_*` methods instead of `getattr()`, avoiding repeated attribute resolution overhead. When methods are found, calls them as unbound methods with `self` as first argument, saving micro-lookups.
4. **Early Exit Optimizations**: Multiple checks for `self.found_any_target_function` throughout the traversal ensure minimal work when target functions are found early.
**Performance Impact Analysis:**
The optimizations are most effective for **large-scale AST processing**:
- Simple ASTs show modest gains (402-508% faster)
- Large ASTs with 1000+ nodes show dramatic improvements (6839% faster for 1000 assignments)
- Complex nested structures benefit significantly (976% faster for deeply nested ASTs)
However, the optimizations introduce small overhead for very simple cases:
- Empty modules and nodes with no fields are 20-33% slower due to additional local variable setup
- The setup cost is amortized quickly as AST complexity increases
**Ideal Use Cases:**
These optimizations excel when processing large codebases, complex AST structures, or when the analyzer is used in hot paths where AST traversal performance is critical. The dramatic speedups on realistic code sizes (1000+ node ASTs) make this particularly valuable for code analysis tools that need to process many files efficiently.
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