⚡️ Speed up function `sorter` by 197,257% #338

codeflash-ai · 2025-06-17T21:38:19Z

📄 197,257% (1,972.57x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`

⏱️ Runtime : 3.36 seconds → 1.70 milliseconds (best of 574 runs)

📝 Explanation and details

Here is a faster rewriting of your program. Your original code uses an unoptimized bubble sort (O(n²) time), but Python's built-in sort is highly optimized (Timsort, O(n log n) on average), while maintaining the same output and function signature.

Preserved all your print statements and comments structure.
No unnecessary allocations.
Function output stays the same.

Explanation:

arr.sort() does the sorting way faster and in-place (i.e., no extra memory unless needed internally).
No need to rewrite the print statements.

If you want to keep a manual sorting algorithm for instructional reasons but still speed it up:

You can implement an optimized bubble sort that stops if no swaps occurred in a pass, but for performance, using .sort() is the best in real-world Python.

If you want the fastest code, use what's above!
If you're required to keep your own bubble sort, let me know!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 57 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests Details

benchmarks/test_benchmark_bubble_sort.py
- $$\color{green}\texttt{test_sort2}: 6.82ms \rightarrow 16.8μs$$
test_bubble_sort.py
- $\color{green}test_sort: 822ms \rightarrow 141μs$
test_bubble_sort_conditional.py
- $\color{green}test_sort: 5.75μs \rightarrow 3.25μs$
test_bubble_sort_import.py
- $\color{green}test_sort: 820ms \rightarrow 141μs$
test_bubble_sort_in_class.py
- $\color{green}TestSorter.test_sort_in_pytest_class: 824ms \rightarrow 143μs$
test_bubble_sort_parametrized.py
- $\color{green}test_sort_parametrized: 501ms \rightarrow 140μs$
test_bubble_sort_parametrized_loop.py
- $\color{green}test_sort_loop_parametrized: 104μs \rightarrow 21.0μs$

🌀 Generated Regression Tests Details

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used 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[:]) # 4.75μs -> 3.17μs

def test_sorter_basic_unsorted():
    # Unsorted list should be sorted
    arr = [5, 3, 1, 4, 2]
    codeflash_output = sorter(arr[:]) # 4.88μs -> 3.25μs

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr[:]) # 5.04μs -> 3.00μs

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [3, 1, 2, 3, 1]
    codeflash_output = sorter(arr[:]) # 4.88μs -> 2.96μs

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr[:]) # 3.96μs -> 3.17μs

def test_sorter_basic_two_elements():
    # Two element list, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr[:]) # 4.04μs -> 2.92μs

def test_sorter_basic_two_elements_sorted():
    # Two element list, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr[:]) # 3.92μs -> 2.92μs

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2]
    codeflash_output = sorter(arr[:]) # 4.71μs -> 3.12μs

def test_sorter_basic_floats():
    # List with floats and integers
    arr = [3.5, 2.1, 2, 1.9]
    codeflash_output = sorter(arr[:]) # 6.50μs -> 3.38μs

def test_sorter_basic_strings():
    # List of strings
    arr = ['banana', 'apple', 'cherry']
    codeflash_output = sorter(arr[:]) # 4.75μs -> 3.25μs

def test_sorter_basic_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr[:]) # 3.62μs -> 2.92μs

# -------------------------
# 2. EDGE TEST CASES
# -------------------------

def test_sorter_edge_all_equal():
    # All elements are equal
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr[:]) # 4.50μs -> 3.12μs

def test_sorter_edge_large_and_small():
    # List with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    codeflash_output = sorter(arr[:]) # 4.88μs -> 3.21μs

def test_sorter_edge_mixed_types():
    # List with mixed types should raise TypeError in Python 3
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr[:])

def test_sorter_edge_strings_case():
    # List of strings with different cases
    arr = ['banana', 'Apple', 'cherry']
    # Default sorting is case-sensitive: uppercase < lowercase
    codeflash_output = sorter(arr[:]) # 4.42μs -> 3.21μs

def test_sorter_edge_unicode_strings():
    # List of unicode strings
    arr = ['éclair', 'apple', 'Éclair']
    # Default sorting: capital accented comes before lowercase
    codeflash_output = sorter(arr[:]) # 5.21μs -> 3.46μs

def test_sorter_edge_single_empty_string():
    # List with single empty string
    arr = [""]
    codeflash_output = sorter(arr[:]) # 3.92μs -> 2.92μs

def test_sorter_edge_multiple_empty_strings():
    # List with multiple empty strings and others
    arr = ["", "a", "", "b"]
    codeflash_output = sorter(arr[:]) # 4.92μs -> 3.21μs

def test_sorter_edge_large_negative_floats():
    # List with large negative floats
    arr = [-1e10, -1e20, -1e5]
    codeflash_output = sorter(arr[:]) # 6.54μs -> 3.92μs

def test_sorter_edge_nan_inf():
    # List with NaN and infinity
    arr = [float('nan'), float('inf'), -float('inf'), 0]
    # Sorting with NaN: NaN is always placed at the end in Python's sort
    codeflash_output = sorter(arr[:]); result = codeflash_output # 5.29μs -> 3.42μs

def test_sorter_edge_mutation():
    # Ensure original list is mutated (since the function sorts in-place)
    arr = [3, 2, 1]
    sorter(arr)

# -------------------------
# 3. LARGE SCALE TEST CASES
# -------------------------

def test_sorter_large_random_integers():
    # Large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 28.4ms -> 59.5μs

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr[:]) # 18.4ms -> 29.0μs

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr[:]) # 30.4ms -> 29.5μs

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [random.choice([0, 1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 25.2ms -> 52.4μs

def test_sorter_large_strings():
    # Large list of random lowercase strings
    arr = [''.join(random.choices(string.ascii_lowercase, k=5)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 30.3ms -> 93.3μs

def test_sorter_large_floats():
    # Large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 26.6ms -> 288μs

def test_sorter_large_all_equal():
    # Large list with all elements equal
    arr = [42] * 1000
    codeflash_output = sorter(arr[:]) # 18.0ms -> 27.7μs

def test_sorter_large_alternating():
    # Large list with alternating pattern
    arr = [0, 1] * 500
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 21.6ms -> 43.8μs

def test_sorter_large_already_sorted_strings():
    # Large sorted list of strings
    arr = [f"item{str(i).zfill(4)}" for i in range(1000)]
    codeflash_output = sorter(arr[:]) # 21.3ms -> 40.0μs

def test_sorter_large_reverse_sorted_strings():
    # Large reverse sorted list of strings
    arr = [f"item{str(i).zfill(4)}" for i in range(999, -1, -1)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 33.5ms -> 39.8μs
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import random  # used for generating large random test cases
import string  # used for string sorting tests
import sys  # used for maxsize in edge 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
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.12μs

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.08μs

def test_sorter_basic_unsorted():
    # Unsorted list with positive integers
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.08μs

def test_sorter_basic_duplicates():
    # List with duplicate elements
    arr = [4, 2, 5, 2, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.04μs

def test_sorter_basic_negative_numbers():
    # List with negative and positive numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.25μs

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 2.92μs

def test_sorter_basic_two_elements():
    # List with two elements
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.12μs -> 2.96μs

def test_sorter_basic_all_equal():
    # List where all elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.33μs -> 3.00μs

def test_sorter_basic_floats():
    # List with floating point numbers
    arr = [3.1, 2.2, 5.5, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.17μs -> 3.71μs

def test_sorter_basic_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.12μs

# ---------------- Edge Test Cases ----------------

def test_sorter_edge_empty_list():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.67μs -> 2.96μs

def test_sorter_edge_large_numbers():
    # List with very large integers
    arr = [sys.maxsize, -sys.maxsize, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 3.25μs

def test_sorter_edge_small_numbers():
    # List with very small (negative) integers
    arr = [-999999999, -1000000000, -999999998]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.54μs

def test_sorter_edge_mixed_types_raises():
    # List with mixed types should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_nested_lists_raises():
    # List with nested lists should raise TypeError
    arr = [1, [2, 3], 4]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_unicode_strings():
    # List of Unicode strings
    arr = ["ápple", "banana", "äpple", "apple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.50μs -> 3.46μs

def test_sorter_edge_case_sensitive_strings():
    # List of strings with mixed case
    arr = ["Banana", "apple", "Cherry", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.29μs

def test_sorter_edge_mutation():
    # Ensure the original list is mutated (in-place sort)
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 4.71μs -> 3.25μs

# ---------------- 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 # 18.3ms -> 29.2μs

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.1ms -> 30.1μs

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 # 28.3ms -> 58.5μs

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 # 24.6ms -> 51.0μs

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 # 29.7ms -> 94.2μs

# ---------------- Additional Edge Cases ----------------

def test_sorter_edge_nan_floats():
    # List with NaN values (should sort, but NaN is always unordered)
    arr = [float('nan'), 1.0, 2.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.29μs -> 3.50μs

def test_sorter_edge_inf_floats():
    # List with inf and -inf
    arr = [float('inf'), 1.0, float('-inf'), 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.25μs -> 3.42μs

def test_sorter_edge_custom_objects_raises():
    # List with custom objects not supporting comparison
    class A:
        pass
    arr = [A(), A()]
    with pytest.raises(TypeError):
        sorter(arr.copy())
# 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-mc11oni7 and push.

Here is a faster rewriting of your program. Your original code uses an **unoptimized bubble sort** (O(n²) time), but **Python's built-in sort** is highly optimized (Timsort, O(n log n) on average), while maintaining the same output and function signature. **Preserved all your print statements and comments structure.** **No unnecessary allocations.** **Function output stays the same.** **Explanation:** - `arr.sort()` does the sorting **way faster** and in-place (i.e., no extra memory unless needed internally). - No need to rewrite the print statements. **If you want to keep a manual sorting algorithm for instructional reasons but still speed it up:** - You can implement an optimized bubble sort that stops if no swaps occurred in a pass, but for performance, using `.sort()` is the best in real-world Python. If you want the fastest code, use what's above! If you're required to keep your own bubble sort, let me know!

codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Jun 17, 2025

codeflash-ai bot requested a review from aseembits93 June 17, 2025 21:38

aseembits93 closed this Jun 17, 2025

codeflash-ai bot deleted the codeflash/optimize-sorter-mc11oni7 branch June 17, 2025 22:11

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⚡️ Speed up function `sorter` by 197,257% #338

⚡️ Speed up function `sorter` by 197,257% #338

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codeflash-ai bot commented Jun 17, 2025 •

edited by aseembits93

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⚡️ Speed up function sorter by 197,257% #338

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codeflash-ai bot commented Jun 17, 2025 • edited by aseembits93 Loading Uh oh! There was an error while loading. Please reload this page.

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📄 197,257% (1,972.57x) speedup for sorter in code_to_optimize/bubble_sort.py

📝 Explanation and details

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⚡️ Speed up function `sorter` by 197,257% #338

⚡️ Speed up function `sorter` by 197,257% #338

codeflash-ai bot commented Jun 17, 2025 •

edited by aseembits93

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📄 197,257% (1,972.57x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`