⚡️ Speed up method JavaAnalyzer.find_methods by 11% in PR #1199 (omni-java)#1298
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codeflash-ai[bot] wants to merge 1 commit intoomni-javafrom
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⚡️ Speed up method JavaAnalyzer.find_methods by 11% in PR #1199 (omni-java)#1298codeflash-ai[bot] wants to merge 1 commit intoomni-javafrom
JavaAnalyzer.find_methods by 11% in PR #1199 (omni-java)#1298codeflash-ai[bot] wants to merge 1 commit intoomni-javafrom
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The optimized code achieves an **11% runtime improvement** (24.5ms → 22.0ms) by eliminating recursive function call overhead through two key optimizations:
## Primary Optimization: Iterative Tree Traversal
The core improvement replaces recursive calls to `_walk_tree_for_methods` with an explicit stack-based iteration. In Python, each recursive call incurs significant overhead from:
- Stack frame creation and teardown
- Parameter passing (6 parameters per call)
- Return address management
The profiler data confirms this: the original code spent 24.5% of time in recursive call setup (lines 39338 hits at 816.7ns per hit), while the optimized version eliminates this entirely by using a stack data structure.
The iterative approach processes nodes in the same depth-first, left-to-right order (by reversing children before pushing to stack) but replaces ~19,726 recursive function calls with simple stack operations. This is particularly effective for Java code analysis where the AST can have deep nesting (nested classes, methods, etc.).
## Secondary Optimization: Type Declaration Tuple Caching
Moving `type_declarations = ("class_declaration", "interface_declaration", "enum_declaration")` from a local variable allocated on every call (19,726 times) to a single instance attribute `self._type_declarations` eliminates 19,726 tuple allocations. The profiler shows this saved 3.3% of execution time in the original version.
## Performance Characteristics
The optimization excels on test cases with:
- **Many methods** (100+ methods): 11.6-14.6% speedup as recursive overhead compounds
- **Deep nesting** (nested classes): 8.85% speedup by avoiding deep call stacks
- **Large files with filtering**: 11-12% speedup as the stack approach handles conditional logic efficiently
- **Mixed interfaces/classes**: 13.2% speedup due to reduced overhead when tracking type context
The optimization maintains identical correctness across all test cases, preserving method discovery, filtering behavior, line numbers, class tracking, and return types.
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⚡️ This pull request contains optimizations for PR #1199
If you approve this dependent PR, these changes will be merged into the original PR branch
omni-java.📄 11% (0.11x) speedup for
JavaAnalyzer.find_methodsincodeflash/languages/java/parser.py⏱️ Runtime :
24.5 milliseconds→22.0 milliseconds(best of123runs)📝 Explanation and details
The optimized code achieves an 11% runtime improvement (24.5ms → 22.0ms) by eliminating recursive function call overhead through two key optimizations:
Primary Optimization: Iterative Tree Traversal
The core improvement replaces recursive calls to
_walk_tree_for_methodswith an explicit stack-based iteration. In Python, each recursive call incurs significant overhead from:The profiler data confirms this: the original code spent 24.5% of time in recursive call setup (lines 39338 hits at 816.7ns per hit), while the optimized version eliminates this entirely by using a stack data structure.
The iterative approach processes nodes in the same depth-first, left-to-right order (by reversing children before pushing to stack) but replaces ~19,726 recursive function calls with simple stack operations. This is particularly effective for Java code analysis where the AST can have deep nesting (nested classes, methods, etc.).
Secondary Optimization: Type Declaration Tuple Caching
Moving
type_declarations = ("class_declaration", "interface_declaration", "enum_declaration")from a local variable allocated on every call (19,726 times) to a single instance attributeself._type_declarationseliminates 19,726 tuple allocations. The profiler shows this saved 3.3% of execution time in the original version.Performance Characteristics
The optimization excels on test cases with:
The optimization maintains identical correctness across all test cases, preserving method discovery, filtering behavior, line numbers, class tracking, and return types.
✅ Correctness verification report:
🌀 Click to see Generated Regression Tests
To edit these changes
git checkout codeflash/optimize-pr1199-2026-02-03T10.01.07and push.