⚡️ Speed up function `sorter` by 173,341% #590

codeflash-ai · 2025-07-28T21:20:33Z

📄 173,341% (1,733.41x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`

⏱️ Runtime : 3.41 seconds → 1.97 milliseconds (best of 461 runs)

📝 Explanation and details

Looking at the original and optimized code, I can see that the optimization correctly replaces the inefficient bubble sort implementation with Python's built-in sort() method, which provides a massive performance improvement (169445% faster).

However, I notice that the optimized code includes a new comment that wasn't in the original code: # Using the built-in sort() method for better performance. According to the refinement rules, I should revert new comments that are different from the original source code unless the new code is complex and requires additional context. In this case, the optimization is straightforward (just replacing the nested loops with arr.sort()), so the comment is not necessary.

Let me refine the optimized code by removing this added comment to make it more precise and closer to the original structure:

<replace_in_file>
optimized_source_code

<<<<<<< SEARCH
def sorter(arr):
print("codeflash stdout: Sorting list")
# Using the built-in sort() method for better performance
arr.sort()
print(f"result: {arr}")
return arr

def sorter(arr):
print("codeflash stdout: Sorting list")
arr.sort()
print(f"result: {arr}")
return arr

REPLACE

This refinement:

Preserves the main optimization (using arr.sort() instead of bubble sort)
Removes the unnecessary comment to make the code more precise and closer to the original
Maintains identical behavior and performance characteristics
Reduces the number of lines different between the original and optimized code

The core optimization remains intact - replacing the O(n²) bubble sort with Python's highly optimized built-in sort algorithm, which provides the massive performance improvement while keeping the code clean and minimal.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 59 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
`benchmarks/test_benchmark_bubble_sort.py::test_sort2`	6.97ms	17.1μs	✅40612%
`test_bubble_sort.py::test_sort`	826ms	131μs	✅630496%
`test_bubble_sort_conditional.py::test_sort`	5.83μs	3.17μs	✅84.2%
`test_bubble_sort_import.py::test_sort`	825ms	134μs	✅615149%
`test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class`	828ms	132μs	✅625223%
`test_bubble_sort_parametrized.py::test_sort_parametrized`	503ms	130μs	✅384922%
`test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized`	102μs	21.2μs	✅385%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting test cases

# imports
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort import sorter

# unit tests

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

def test_sorter_basic_sorted():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 3.12μs (86.7% faster)

def test_sorter_basic_reverse():
    # Reversed list should become sorted
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.62μs -> 3.04μs (84.9% faster)

def test_sorter_basic_unsorted():
    # Random order list should be sorted
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 3.04μs (65.8% faster)

def test_sorter_basic_duplicates():
    # List with duplicates should be sorted and maintain duplicates
    arr = [4, 2, 2, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.04μs (61.6% faster)

def test_sorter_basic_all_equal():
    # All elements equal should remain unchanged
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.29μs -> 2.96μs (45.0% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-2, -5, 3, 1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 3.08μs (66.2% faster)

def test_sorter_basic_mixed_signs():
    # List with both positive and negative numbers
    arr = [0, -1, 2, -3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.08μs (55.4% faster)

def test_sorter_basic_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.96μs -> 2.96μs (33.8% faster)

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

def test_sorter_edge_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.67μs -> 2.88μs (27.5% faster)

def test_sorter_edge_large_numbers():
    # List with very large and very small integers
    arr = [999999999, -999999999, 0, 2147483647, -2147483648]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.42μs -> 3.58μs (79.1% faster)

def test_sorter_edge_floats():
    # List with floats and integers
    arr = [3.5, 2, -1.1, 0, 2.2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.25μs -> 3.92μs (85.1% faster)

def test_sorter_edge_strings():
    # List of strings should be sorted lexicographically
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.17μs (52.6% faster)

def test_sorter_edge_case_sensitive_strings():
    # List of strings with different cases (upper/lower)
    arr = ["banana", "Apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 3.12μs (44.0% faster)

def test_sorter_edge_empty_strings():
    # List with empty strings and normal strings
    arr = ["", "a", "abc", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 3.21μs (57.1% faster)

def test_sorter_edge_single_type_error():
    # List with incomparable types should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.83μs -> 2.00μs (41.6% faster)


def test_sorter_edge_none_in_list():
    # List with None and numbers should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.04μs -> 2.04μs (48.9% faster)

def test_sorter_edge_mutation():
    # Ensure input list is mutated in place (bubble sort is in-place)
    arr = [3, 2, 1]
    sorter(arr) # 4.83μs -> 3.12μs (54.7% faster)

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

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.4ms -> 30.3μs (60777% faster)

def test_sorter_large_reverse():
    # Large reversed list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.6ms -> 29.5μs (103517% faster)

def test_sorter_large_random():
    # Large random list of integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.7ms -> 62.4μs (44301% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 24.8ms -> 50.4μs (49046% faster)

def test_sorter_large_strings():
    # Large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.8ms -> 92.5μs (32086% faster)

def test_sorter_large_all_equal():
    # Large list where all elements are equal
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 17.8ms -> 28.0μs (63443% faster)

def test_sorter_large_floats():
    # Large list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.1ms -> 280μs (9562% faster)

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

def test_sorter_edge_minimal_two_elements():
    # Two element list, sorted and unsorted
    arr1 = [1, 2]
    arr2 = [2, 1]
    codeflash_output = sorter(arr1.copy()) # 4.75μs -> 3.08μs (54.0% faster)
    codeflash_output = sorter(arr2.copy()) # 3.79μs -> 2.88μs (31.9% faster)

def test_sorter_edge_stability():
    # Check stability of sort for equal elements (should preserve order for equal elements)
    class StableObj:
        def __init__(self, key, label):
            self.key = key
            self.label = label
        def __lt__(self, other):
            return self.key < other.key
        def __gt__(self, other):
            return self.key > other.key
        def __eq__(self, other):
            return self.key == other.key and self.label == other.label
        def __repr__(self):
            return f"StableObj({self.key}, {self.label})"
    arr = [StableObj(1, 'a'), StableObj(1, 'b'), StableObj(2, 'c')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.17μs -> 4.33μs (42.3% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import random  # for generating large random lists
import string  # for string sorting tests
import sys  # for maxsize in edge cases

# imports
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort import sorter

# unit tests

# ---------------------
# 1. Basic Test Cases
# ---------------------

def test_sorter_basic_sorted():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 3.04μs (56.1% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.04μs (69.8% faster)

def test_sorter_basic_unsorted():
    # Unsorted list should be sorted in ascending order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.04μs (64.4% faster)

def test_sorter_basic_duplicates():
    # List with duplicates should be sorted and retain duplicates
    arr = [4, 2, 2, 5, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 2.96μs (62.0% faster)

def test_sorter_basic_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.83μs -> 3.00μs (27.8% faster)

def test_sorter_basic_empty():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.58μs -> 2.75μs (30.3% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers should be sorted correctly
    arr = [-3, -1, -4, -2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 3.29μs (53.2% faster)

def test_sorter_basic_mixed_signs():
    # List with both positive and negative numbers
    arr = [0, -2, 5, -1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 3.08μs (51.3% faster)

def test_sorter_basic_floats():
    # List with floats and integers
    arr = [3.2, 1.5, 2, 4.8, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.58μs -> 3.92μs (68.1% faster)

def test_sorter_basic_strings():
    # List of strings should be sorted lexicographically
    arr = ['banana', 'apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 3.17μs (38.1% faster)

# ---------------------
# 2. Edge Test Cases
# ---------------------

def test_sorter_edge_all_identical():
    # All elements identical
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.17μs -> 3.00μs (38.9% faster)

def test_sorter_edge_large_and_small_numbers():
    # List with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.17μs -> 3.62μs (70.1% faster)

def test_sorter_edge_single_string():
    # Single string element
    arr = ['hello']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.62μs -> 3.04μs (19.2% faster)

def test_sorter_edge_empty_string():
    # List with empty string and other strings
    arr = ['', 'a', 'b']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.21μs (42.9% faster)

def test_sorter_edge_unicode_strings():
    # List with unicode strings
    arr = ['😀', '😃', '😄', '😁']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.04μs -> 4.21μs (43.5% faster)

def test_sorter_edge_case_sensitive_strings():
    # List with case-sensitive strings
    arr = ['apple', 'Banana', 'banana', 'Apple']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.29μs (57.0% faster)

def test_sorter_edge_minimal_list():
    # List with two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.96μs -> 2.96μs (33.8% faster)

def test_sorter_edge_minimal_list_unsorted():
    # List with two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.71μs -> 2.92μs (27.2% faster)

def test_sorter_edge_float_precision():
    # List with floats of close values
    arr = [1.000001, 1.0000001, 1.00001]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.46μs -> 3.71μs (47.2% faster)

def test_sorter_edge_negative_zero():
    # List with -0.0 and 0.0, which compare equal but may have different representations
    arr = [-0.0, 0.0, 1.0, -1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.29μs -> 3.29μs (60.7% faster)

def test_sorter_edge_large_range():
    # List with a large range of numbers, including negatives and positives
    arr = list(range(-50, 51, 10))  # [-50, -40, ..., 40, 50]
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.38μs -> 3.50μs (168% faster)

def test_sorter_edge_mutation():
    # Ensure that the function sorts in-place and returns the same object (since that's what the implementation does)
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 4.50μs -> 3.00μs (50.0% faster)

# ---------------------
# 3. Large Scale Test Cases
# ---------------------

def test_sorter_large_random_integers():
    # Large list of random integers
    arr = [random.randint(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.5ms -> 58.5μs (48687% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.5ms -> 28.4μs (64928% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.9ms -> 28.2μs (109320% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 24.8ms -> 48.0μs (51508% faster)

def test_sorter_large_strings():
    # Large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.6ms -> 90.9μs (32522% faster)

def test_sorter_large_floats():
    # Large list of random floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.1ms -> 276μs (9727% faster)

def test_sorter_large_already_sorted_with_duplicates():
    # Large list, already sorted, with many duplicates
    arr = sorted([random.choice([10, 20, 30, 40, 50]) for _ in range(1000)])
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.3ms -> 27.0μs (67615% faster)

def test_sorter_large_negative_numbers():
    # Large list of negative numbers
    arr = [random.randint(-1000, -1) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.3ms -> 56.5μs (48233% faster)

def test_sorter_large_one_value():
    # Large list with the same value repeated
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 17.9ms -> 26.8μs (66501% faster)

def test_sorter_large_alternating():
    # Large list alternating between two values
    arr = [0, 1] * 500
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 22.6ms -> 51.3μs (43993% 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-sorter-mdnm3q8d and push.

Looking at the original and optimized code, I can see that the optimization correctly replaces the inefficient bubble sort implementation with Python's built-in `sort()` method, which provides a massive performance improvement (169445% faster). However, I notice that the optimized code includes a new comment that wasn't in the original code: `# Using the built-in sort() method for better performance`. According to the refinement rules, I should revert new comments that are different from the original source code unless the new code is complex and requires additional context. In this case, the optimization is straightforward (just replacing the nested loops with `arr.sort()`), so the comment is not necessary. Let me refine the optimized code by removing this added comment to make it more precise and closer to the original structure: <replace_in_file> <path>optimized_source_code</path> <diff> <<<<<<< SEARCH def sorter(arr): print("codeflash stdout: Sorting list") # Using the built-in sort() method for better performance arr.sort() print(f"result: {arr}") return arr ======= def sorter(arr): print("codeflash stdout: Sorting list") arr.sort() print(f"result: {arr}") return arr >>>>>>> REPLACE </diff> </replace_in_file> This refinement: 1. Preserves the main optimization (using `arr.sort()` instead of bubble sort) 2. Removes the unnecessary comment to make the code more precise and closer to the original 3. Maintains identical behavior and performance characteristics 4. Reduces the number of lines different between the original and optimized code The core optimization remains intact - replacing the O(n²) bubble sort with Python's highly optimized built-in sort algorithm, which provides the massive performance improvement while keeping the code clean and minimal.

codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Jul 28, 2025

codeflash-ai bot requested a review from aseembits93 July 28, 2025 21:20

aseembits93 closed this Jul 28, 2025

codeflash-ai bot deleted the codeflash/optimize-sorter-mdnm3q8d branch July 28, 2025 21:22

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⚡️ Speed up function `sorter` by 173,341% #590

⚡️ Speed up function `sorter` by 173,341% #590

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⚡️ Speed up function sorter by 173,341% #590

⚡️ Speed up function sorter by 173,341% #590

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codeflash-ai bot commented Jul 28, 2025

📄 173,341% (1,733.41x) speedup for sorter in code_to_optimize/bubble_sort.py

📝 Explanation and details

<replace_in_file> optimized_source_code <<<<<<< SEARCH def sorter(arr): print("codeflash stdout: Sorting list") # Using the built-in sort() method for better performance arr.sort() print(f"result: {arr}") return arr

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⚡️ Speed up function `sorter` by 173,341% #590

⚡️ Speed up function `sorter` by 173,341% #590

📄 173,341% (1,733.41x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`

<replace_in_file>
optimized_source_code

<<<<<<< SEARCH
def sorter(arr):
print("codeflash stdout: Sorting list")
# Using the built-in sort() method for better performance
arr.sort()
print(f"result: {arr}")
return arr