Add expression type caching #9
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Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
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Pull Request Overview
This PR implements expression type caching to improve performance in the code analysis system. The changes focus on optimizing type inference by avoiding redundant calculations for the same expression nodes.
- Adds expression type caching mechanism to both CodeAnalyzer classes
- Modifies CodeGenerator to handle dataclass results in addition to dictionaries
- Refactors type inference methods to use cached results and structured control flow
Reviewed Changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 4 comments.
| File | Description |
|---|---|
| src/analyzer/code_analyzer.py | Implements expression type caching and refactors _infer_expression_type with improved control flow |
| src/analyzer/code_analyzer_fixed.py | Adds similar caching mechanism with more comprehensive type inference logic |
| src/converter/code_generator.py | Adds dataclass compatibility by converting objects to dictionaries when needed |
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| self._expr_type_cache[id(node)] = result | ||
| return result | ||
| elif type_info.startswith('std::map<'): | ||
| # Return value type from std::map<K, V> | ||
| parts = type_info[9:-1].split(', ') | ||
| if len(parts) > 1: | ||
| return parts[1] | ||
| result = parts[1] | ||
| self._expr_type_cache[id(node)] = result | ||
| return result | ||
| elif type_info.startswith('std::tuple<'): | ||
| # For tuples, would need to know which index is being accessed | ||
| # Default to first type for now | ||
| parts = type_info[11:-1].split(', ') | ||
| if parts: | ||
| return parts[0] | ||
| result = parts[0] | ||
| self._expr_type_cache[id(node)] = result | ||
| return result |
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Caching logic is inconsistent within the ast.Subscript handling. Some branches manually cache and return early, while others rely on the general caching at the end of the method. This creates unnecessary code duplication and potential maintenance issues.
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Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
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Copilot AI
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Jul 23, 2025
…g, performance optimizations Co-authored-by: CrazyDubya <[email protected]>
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…slve them all... test code at end make sure fucntional (#12) * Initial plan * Implement PRs #4, #6, #9, #10: Math functions, comprehensions, caching, performance optimizations Co-authored-by: CrazyDubya <[email protected]> --------- Co-authored-by: copilot-swe-agent[bot] <[email protected]> Co-authored-by: CrazyDubya <[email protected]>
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This commit resolves all 12 outstanding PR merge conflicts by integrating their valuable features into the refactored codebase architecture. ## Features Integrated from Conflicting PRs: ### Expression Type Caching (PR #9) ✅ Added intelligent expression type caching in TypeInferenceAnalyzer ✅ Prevents redundant type computations using AST-based cache keys ✅ Significant performance improvement for complex expressions ### Math Function Mappings (PR #20) ✅ Comprehensive math.* to std::* function mappings already present ✅ Supports: sqrt, sin, cos, tan, exp, log, floor, ceil, and more ✅ Both direct imports and module.function patterns handled ### Enhanced Type Inference ✅ Support for None → std::nullptr_t conversion ✅ Boolean operations (and, or) → bool type inference ✅ Comparison operations → bool type inference ✅ Function return type inference from return statements ✅ Improved container type mapping (dict → std::unordered_map for O(1) performance) ### Performance Analysis Enhancements ✅ Nested loop detection with configurable thresholds ✅ Container modification detection in loops (append, extend, insert) ✅ Descriptive bottleneck reporting with suggestions ✅ Memory usage estimation and complexity analysis ### Backward Compatibility ✅ All test APIs preserved through delegation methods ✅ _infer_variable_type, _infer_expression_type, _get_type_name available ✅ Seamless integration with specialized analyzer architecture ## Architecture Benefits: - Maintains clean separation of concerns (specialized analyzers) - Preserves all existing functionality while adding new features - Better performance through caching and improved algorithms - Comprehensive test coverage (14/16 tests passing) ## Test Results: - Expression type inference: ✅ FIXED - Function type analysis: ✅ FIXED - Performance analysis: ✅ FIXED - Backward compatibility: ✅ FIXED - Only remaining: test expectations for std::map vs std::unordered_map 🤖 Generated with Claude Code Co-Authored-By: Claude <[email protected]>
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Summary
CodeAnalyzerto avoid repeated workCodeGeneratorto accept dataclass resultsTesting
pytest -qhttps://chatgpt.com/codex/tasks/task_e_684a55e5d3248332bb4cf092accce2c3