⚡️ Speed up method ImportAnalyzer._fast_generic_visit by 44% in PR #867 (inspect-signature-issue)

[codeflash-ai]

⚡️ This pull request contains optimizations for PR #867

If you approve this dependent PR, these changes will be merged into the original PR branch inspect-signature-issue.

This PR will be automatically closed if the original PR is merged.

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📄 44% (0.44x) speedup for ImportAnalyzer._fast_generic_visit in codeflash/discovery/discover_unit_tests.py

⏱️ Runtime : 1.09 milliseconds → 756 microseconds (best of 27 runs)

📝 Explanation and details

The optimization converts the recursive AST traversal from a call-stack based approach to an iterative one using a manual stack, delivering a 44% performance improvement.

Key optimizations applied:

1. Stack-based iteration replaces recursion: The original code used recursive calls to _fast_generic_visit() and meth() for AST traversal. The optimized version uses a manual stack with while loop iteration, eliminating function call overhead and stack frame management costs.

2. Faster method resolution: Replaced getattr(self, "visit_" + classname, None) with type(self).__dict__.get("visit_" + classname), which is significantly faster for method lookup. The class dictionary lookup avoids the more expensive attribute resolution pathway.

3. Local variable caching: Pre-cached frequently accessed attributes like stack.append, stack.pop, and type(self).__dict__ into local variables to reduce repeated attribute lookups during the tight inner loop.

Why this leads to speedup:

• Reduced function call overhead: Each recursive call in the original version creates a new stack frame with associated setup/teardown costs. The iterative approach eliminates this entirely.
• Faster method resolution: Dictionary .get() is ~2-3x faster than getattr() for method lookups, especially important since this happens for every AST node visited.
• Better cache locality: The manual stack keeps traversal state in a more compact, cache-friendly format compared to Python's call stack.

Performance characteristics from test results:

The optimization shows variable performance depending on AST structure:

• Large nested trees: 39.2% faster (deep recursion → iteration benefit is maximized)
• Early exit scenarios: 57% faster on large trees (stack-based approach handles early termination more efficiently)
• Simple nodes: Some overhead for very small cases due to setup costs, but still performs well on realistic workloads
• Complex traversals: 14-24% faster on typical code structures with mixed node types

This optimization is particularly valuable for AST analysis tools that process large codebases, where the cumulative effect of faster traversal becomes significant.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 34 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	90.5%

🌀 Generated Regression Tests and Runtime

import ast

# imports
import pytest
from codeflash.discovery.discover_unit_tests import ImportAnalyzer

# unit tests

# Helper function to create a dummy visitor with a custom visit method
class DummyVisitor(ast.NodeVisitor):
    def __init__(self):
        self.visited_nodes = []
    def visit_Name(self, node):
        self.visited_nodes.append(('Name', node.id))
    def visit_Constant(self, node):
        self.visited_nodes.append(('Constant', node.value))
    def visit(self, node):
        # For test: call generic_visit
        self._fast_generic_visit(node)

# Patch DummyVisitor with _fast_generic_visit from ImportAnalyzer
DummyVisitor._fast_generic_visit = ImportAnalyzer._fast_generic_visit

# --- BASIC TEST CASES ---


















#------------------------------------------------
import ast

# imports
import pytest
from codeflash.discovery.discover_unit_tests import ImportAnalyzer

# unit tests

# Helper: a dummy visitor class that records the order of nodes visited
class RecordingVisitor(ast.NodeVisitor):
    def __init__(self):
        self.visited = []

    def generic_visit(self, node):
        self.visited.append(type(node).__name__)
        super().generic_visit(node)

# Helper: a dummy visitor with a flag for early exit
class EarlyExitVisitor(ImportAnalyzer):
    def __init__(self):
        super().__init__(set())
        self.order = []

    def visit_Name(self, node):
        self.order.append(node.id)
        if node.id == "stop":
            self.found_any_target_function = True

# ---------- BASIC TEST CASES ----------

def test_empty_node_list():
    # Test visiting a node with no fields (e.g., ast.Pass)
    analyzer = ImportAnalyzer(set())
    node = ast.Pass()
    analyzer._fast_generic_visit(node) # 841ns -> 1.88μs (55.4% slower)

def test_single_child_node():
    # Test visiting a node with a single AST child (e.g., ast.Expr with ast.Constant)
    analyzer = ImportAnalyzer(set())
    node = ast.Expr(value=ast.Constant(value=42))
    # Should traverse to the Constant node
    recorder = RecordingVisitor()
    recorder.generic_visit(node)
    analyzer._fast_generic_visit(node) # 4.79μs -> 3.88μs (23.5% faster)
    # No assertion needed, just ensure no error

def test_list_of_child_nodes():
    # Test visiting a node with a list of AST children (e.g., ast.Module with body)
    analyzer = ImportAnalyzer(set())
    node = ast.Module(body=[ast.Pass(), ast.Break()], type_ignores=[])
    # Should traverse both Pass and Break nodes
    recorder = RecordingVisitor()
    recorder.generic_visit(node)
    analyzer._fast_generic_visit(node) # 3.07μs -> 4.20μs (26.9% slower)

def test_custom_visit_method_called():
    # Test that custom visit methods are called if present
    class MyVisitor(ImportAnalyzer):
        def __init__(self):
            super().__init__(set())
            self.called = []

        def visit_Name(self, node):
            self.called.append(node.id)
    tree = ast.parse("a = b + c")
    visitor = MyVisitor()
    visitor._fast_generic_visit(tree) # 9.30μs -> 7.94μs (17.0% faster)

def test_generic_visit_fallback():
    # Test that generic_visit fallback works for nodes with no custom visit method
    class MyVisitor(ImportAnalyzer):
        def __init__(self):
            super().__init__(set())
            self.generic_visited = []

        def visit_Constant(self, node):
            self.generic_visited.append(node.value)
    tree = ast.parse("x = 123")
    visitor = MyVisitor()
    visitor._fast_generic_visit(tree) # 8.44μs -> 6.79μs (24.2% faster)

# ---------- EDGE TEST CASES ----------

def test_early_exit_on_found_flag():
    # Test that traversal stops early if found_any_target_function is True
    tree = ast.parse("a = stop\nb = keep")
    visitor = EarlyExitVisitor()
    visitor._fast_generic_visit(tree) # 5.50μs -> 8.23μs (33.2% slower)

def test_node_with_non_ast_field():
    # Test that fields which are not AST or list are ignored (e.g., ast.Constant.value)
    class MyVisitor(ImportAnalyzer):
        def __init__(self):
            super().__init__(set())
            self.constants = []

        def visit_Constant(self, node):
            self.constants.append(node.value)
    tree = ast.parse("x = 42")
    visitor = MyVisitor()
    visitor._fast_generic_visit(tree) # 7.99μs -> 6.71μs (18.9% faster)

def test_node_with_empty_list_field():
    # Node with an empty list field should not error or visit anything
    analyzer = ImportAnalyzer(set())
    node = ast.Module(body=[], type_ignores=[])
    analyzer._fast_generic_visit(node) # 1.15μs -> 2.01μs (42.8% slower)

def test_node_with_none_field():
    # Node with a field set to None should not error
    class DummyNode(ast.AST):
        _fields = ('foo',)
        def __init__(self):
            self.foo = None
    node = DummyNode()
    analyzer = ImportAnalyzer(set())
    analyzer._fast_generic_visit(node) # 1.24μs -> 1.98μs (37.4% slower)

def test_node_with_mixed_fields():
    # Node with a mix of AST, list, None, and primitive fields
    class DummyNode(ast.AST):
        _fields = ('foo', 'bar', 'baz', 'qux')
        def __init__(self):
            self.foo = ast.Pass()
            self.bar = [ast.Break(), ast.Continue()]
            self.baz = None
            self.qux = 123
    node = DummyNode()
    analyzer = ImportAnalyzer(set())
    analyzer._fast_generic_visit(node) # 4.69μs -> 5.84μs (19.7% slower)

# ---------- LARGE SCALE TEST CASES ----------


def test_performance_large_nested_tree():
    # Build a deeply nested AST tree
    N = 300  # keep depth under 1000
    node = ast.Constant(value=0)
    for i in range(N):
        node = ast.UnaryOp(op=ast.USub(), operand=node)
    analyzer = ImportAnalyzer(set())
    analyzer._fast_generic_visit(node) # 321μs -> 231μs (39.2% faster)

def test_early_exit_on_large_tree():
    # Test early exit on a large tree
    N = 500
    body = [ast.Assign(targets=[ast.Name(id=f"x{i}", ctx=ast.Store())],
                       value=ast.Constant(value=i)) for i in range(N)]
    # Insert a 'stop' Name in the middle
    body.insert(N//2, ast.Expr(value=ast.Name(id="stop", ctx=ast.Load())))
    tree = ast.Module(body=body, type_ignores=[])
    visitor = EarlyExitVisitor()
    visitor._fast_generic_visit(tree) # 682μs -> 434μs (57.0% faster)
    # Should not visit names after 'stop'
    found_stop = False
    for name in visitor.order:
        if name == "stop":
            found_stop = True
        if found_stop:
            pass

def test_large_list_field_with_none_and_non_ast():
    # Test a list field with a mix of AST, None, and primitive values
    class DummyNode(ast.AST):
        _fields = ('items',)
        def __init__(self, items):
            self.items = items
    items = [ast.Pass(), None, 123, ast.Break()]
    node = DummyNode(items)
    analyzer = ImportAnalyzer(set())
    analyzer._fast_generic_visit(node) # 3.70μs -> 4.76μs (22.3% slower)

# ---------- ADDITIONAL EDGE CASES ----------

def test_node_with_custom__fields():
    # Node with custom _fields tuple including non-existent attributes
    class DummyNode(ast.AST):
        _fields = ('foo', 'bar')
        def __init__(self):
            self.foo = ast.Pass()
            # 'bar' is missing
    node = DummyNode()
    analyzer = ImportAnalyzer(set())
    analyzer._fast_generic_visit(node) # 2.81μs -> 3.67μs (23.2% slower)

def test_node_with_empty__fields():
    # Node with empty _fields tuple
    class DummyNode(ast.AST):
        _fields = ()
        def __init__(self):
            pass
    node = DummyNode()
    analyzer = ImportAnalyzer(set())
    analyzer._fast_generic_visit(node) # 752ns -> 1.53μs (50.9% slower)

def test_node_with_private_fields():
    # Node with _fields including private attributes (should be ignored)
    class DummyNode(ast.AST):
        _fields = ('_private',)
        def __init__(self):
            self._private = ast.Pass()
    node = DummyNode()
    analyzer = ImportAnalyzer(set())
    analyzer._fast_generic_visit(node) # 2.25μs -> 3.15μs (28.3% slower)

def test_traverse_tree_with_various_node_types():
    # Traverse a tree with various node types to ensure all are handled
    tree = ast.parse("def f(x): return x + 1\nfor i in range(3): print(i)")
    analyzer = ImportAnalyzer(set())
    analyzer._fast_generic_visit(tree) # 31.6μs -> 27.7μs (14.0% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-pr867-2025-11-05T09.44.48 and push.