⚡️ Speed up function _reverse by 5%

[codeflash-ai]

📄 5% (0.05x) speedup for _reverse in openhands/resolver/patching/apply.py

⏱️ Runtime : 1.53 millisecond → 1.45 millisecond (best of 836 runs)

📝 Explanation and details

The code was already well-optimized. Using a list comprehension to apply the _reverse_change function is both concise and efficient. The _reverse_change function has been inlined within the comprehension for slightly improved readability, avoiding the overhead of a nested function.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 20 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	✅ 1 Passed
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests Details

from collections import \
    namedtuple  # used to create a mock Change class for testing

# imports
import pytest  # used for our unit tests
from openhands.resolver.patching.apply import _reverse

# function to test
# -*- coding: utf-8 -*-

# Mock Change class for testing purposes
Change = namedtuple('Change', 'old new')
from openhands.resolver.patching.apply import _reverse

# unit tests

def test_simple_swap():
    # Test a single Change object with distinct old and new values
    change = Change(old='A', new='B')
    codeflash_output = _reverse([change]); result = codeflash_output

def test_multiple_changes():
    # Test a list of multiple Change objects
    changes = [Change(old='A', new='B'), Change(old='C', new='D')]
    codeflash_output = _reverse(changes); result = codeflash_output
    expected = [Change(old='B', new='A'), Change(old='D', new='C')]

def test_empty_list():
    # Test an empty list of Change objects
    codeflash_output = _reverse([]); result = codeflash_output

def test_identical_old_new():
    # Test Change objects where old and new are the same
    change = Change(old='A', new='A')
    codeflash_output = _reverse([change]); result = codeflash_output

def test_mixed_types():
    # Test Change objects with different data types for old and new
    change = Change(old=1, new='A')
    codeflash_output = _reverse([change]); result = codeflash_output

def test_nested_changes():
    # Test Change objects that contain other Change objects as their old or new values
    nested_change = Change(old='X', new='Y')
    change = Change(old=nested_change, new='Z')
    codeflash_output = _reverse([change]); result = codeflash_output

def test_large_list_of_changes():
    # Test a very large list of Change objects to test performance and scalability
    changes = [Change(old=i, new=i+1) for i in range(1000)]
    codeflash_output = _reverse(changes); result = codeflash_output
    expected = [Change(old=i+1, new=i) for i in range(1000)]

def test_immutable_changes():
    # Ensure that the original Change objects are not modified
    original_changes = [Change(old='A', new='B'), Change(old='C', new='D')]
    _reverse(original_changes)

def test_consistent_output():
    # Ensure that given the same input, the function always produces the same output
    changes = [Change(old='A', new='B'), Change(old='C', new='D')]
    codeflash_output = _reverse(changes); result1 = codeflash_output
    codeflash_output = _reverse(changes); result2 = codeflash_output
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from collections import namedtuple

# imports
import pytest  # used for our unit tests
from openhands.resolver.patching.apply import _reverse

# Define a mock Change class for testing purposes
Change = namedtuple('Change', ['old', 'new'])
from openhands.resolver.patching.apply import _reverse

# unit tests

# Basic Functionality Tests
def test_single_change_object():
    # Test reversing a single Change object
    change = Change(old='A', new='B')
    codeflash_output = _reverse([change]); reversed_changes = codeflash_output

def test_multiple_change_objects():
    # Test reversing multiple Change objects
    changes = [Change(old='A', new='B'), Change(old='X', new='Y')]
    codeflash_output = _reverse(changes); reversed_changes = codeflash_output
    expected = [Change(old='B', new='A'), Change(old='Y', new='X')]

# Edge Case Tests
def test_empty_list():
    # Test reversing an empty list
    changes = []
    codeflash_output = _reverse(changes); reversed_changes = codeflash_output

def test_identical_old_new_values():
    # Test a Change object with identical old and new values
    change = Change(old='same', new='same')
    codeflash_output = _reverse([change]); reversed_changes = codeflash_output

def test_mixed_data_types():
    # Test a Change object with mixed data types
    change = Change(old=123, new='123')
    codeflash_output = _reverse([change]); reversed_changes = codeflash_output

# Large Scale Test Cases
def test_large_list_of_changes():
    # Test reversing a large list of Change objects
    changes = [Change(old=i, new=i+1) for i in range(1000)]
    codeflash_output = _reverse(changes); reversed_changes = codeflash_output
    expected = [Change(old=i+1, new=i) for i in range(1000)]

# Error Handling and Invalid Inputs
def test_non_change_objects():
    # Test a list containing non-Change objects
    changes = [Change(old='A', new='B'), 'not a change', 123]
    with pytest.raises(AttributeError):
        _reverse(changes)

# Complex Scenarios
def test_nested_changes():
    # Test a Change object where old and new are dictionaries
    change = Change(old={'key': 'value'}, new={'key': 'new_value'})
    codeflash_output = _reverse([change]); reversed_changes = codeflash_output

# Performance and Scalability
def test_stress_test():
    # Test reversing a very large list of Change objects
    changes = [Change(old=i, new=i+1) for i in range(1000)]
    codeflash_output = _reverse(changes); reversed_changes = codeflash_output
    expected = [Change(old=i+1, new=i) for i in range(1000)]

# Deterministic Behavior
def test_consistent_output():
    # Test that repeated calls with the same input yield the same output
    changes = [Change(old='A', new='B')]
    codeflash_output = _reverse(changes)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from openhands.resolver.patching.apply import _reverse
from openhands.resolver.patching.patch import Change
import pytest

def test__reverse():
    with pytest.raises(TypeError, match='_map\\.<locals>\\.<lambda>\\(\\)\\ takes\\ 1\\ positional\\ argument\\ but\\ 2\\ were\\ given'):
        _reverse([Change('', '', '', '')])

def test__reverse_2():
    _reverse([])

To edit these changes git checkout codeflash/optimize-_reverse-m8wta33t and push.