⚡️ Speed up function sorter by 4211531.56 in PR #107 (temp)
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The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
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Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
There was a problem hiding this comment.
⚡️ Codeflash found optimizations for this PR
📄 4211531.56 (42115.32) speedup for sorter in code_to_optimize/bubble_sort.py
⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)
📝 Explanation and details
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
def sorter(arr):
return sorted(arr)Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | ✅ 39 Passed |
| 🌀 Generated Regression Tests | ✅ 38 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | undefined |
⚙️ Existing Unit Tests Details
- test_bubble_sort.py
- test_bubble_sort__perfinstrumented.py
- test_bubble_sort_conditional.py
- test_bubble_sort_conditional__perfinstrumented.py
- test_bubble_sort_import.py
- test_bubble_sort_import__perfinstrumented.py
- test_bubble_sort_in_class.py
- test_bubble_sort_in_class__perfinstrumented.py
- test_bubble_sort_parametrized.py
- test_bubble_sort_parametrized__perfinstrumented.py
- test_bubble_sort_parametrized_loop.py
- test_bubble_sort_parametrized_loop__perfinstrumented.py
- test_sorter__unit_test_0.py
- test_sorter__unit_test_1.py🌀 Generated Regression Tests Details
# imports
import pytest # used for our unit tests
# function to test
def sorter(arr):
for i in range(len(arr)):
for j in range(len(arr) - 1):
if arr[j] > arr[j + 1]:
temp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = temp
return arr
# unit tests
# Test with an empty list
def test_sorter_empty():
assert sorter([]) == []
# Test with a single-element list
def test_sorter_single_element():
assert sorter([42]) == [42]
# Test with a two-element list
def test_sorter_two_elements():
assert sorter([2, 1]) == [1, 2]
assert sorter([1, 2]) == [1, 2]
# Test with sorted lists
def test_sorter_sorted_list():
assert sorter([1, 2, 3, 4, 5]) == [1, 2, 3, 4, 5]
assert sorter([0, 2, 4, 6, 8, 10]) == [0, 2, 4, 6, 8, 10]
# Test with reverse-sorted lists
def test_sorter_reverse_sorted_list():
assert sorter([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert sorter([-1, -2, -3, -4, -5]) == [-5, -4, -3, -2, -1]
# Test with lists with duplicates
def test_sorter_with_duplicates():
assert sorter([3, 1, 2, 1, 3]) == [1, 1, 2, 3, 3]
assert sorter([5, 5, 5, 5]) == [5, 5, 5, 5]
# Test with lists with negative numbers
def test_sorter_with_negative_numbers():
assert sorter([-1, -3, -2, 0, 2]) == [-3, -2, -1, 0, 2]
assert sorter([-10, 100, -50, 0]) == [-50, -10, 0, 100]
# Test with lists with varying types of numbers
def test_sorter_with_various_number_types():
assert sorter([1.5, 2.3, 1.1, 2.0, 1.9]) == [1.1, 1.5, 1.9, 2.0, 2.3]
assert sorter([1, 2.0, 3, 4.5]) == [1, 2.0, 3, 4.5]
# Test with large lists
def test_sorter_large_list():
large_list = list(range(1000, 0, -1)) # 1000 to 1 in reverse order
assert sorter(large_list) == sorted(large_list)
# Test with lists with non-numeric values
def test_sorter_non_numeric():
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['apple', 'banana', 'cherry'])
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['a', 'aa', 'aaa'])To test or edit this optimization locally git merge codeflash/optimize-pr248-2025-03-25T15.03.09
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
There was a problem hiding this comment.
⚡️ Codeflash found optimizations for this PR
📄 4211531.56 (42115.32) speedup for sorter in code_to_optimize/bubble_sort.py
⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)
📝 Explanation and details
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
def sorter(arr):
return sorted(arr)Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | ✅ 39 Passed |
| 🌀 Generated Regression Tests | ✅ 38 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | undefined |
⚙️ Existing Unit Tests Details
- test_bubble_sort.py
- test_bubble_sort__perfinstrumented.py
- test_bubble_sort_conditional.py
- test_bubble_sort_conditional__perfinstrumented.py
- test_bubble_sort_import.py
- test_bubble_sort_import__perfinstrumented.py
- test_bubble_sort_in_class.py
- test_bubble_sort_in_class__perfinstrumented.py
- test_bubble_sort_parametrized.py
- test_bubble_sort_parametrized__perfinstrumented.py
- test_bubble_sort_parametrized_loop.py
- test_bubble_sort_parametrized_loop__perfinstrumented.py
- test_sorter__unit_test_0.py
- test_sorter__unit_test_1.py🌀 Generated Regression Tests Details
# imports
import pytest # used for our unit tests
# function to test
def sorter(arr):
for i in range(len(arr)):
for j in range(len(arr) - 1):
if arr[j] > arr[j + 1]:
temp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = temp
return arr
# unit tests
# Test with an empty list
def test_sorter_empty():
assert sorter([]) == []
# Test with a single-element list
def test_sorter_single_element():
assert sorter([42]) == [42]
# Test with a two-element list
def test_sorter_two_elements():
assert sorter([2, 1]) == [1, 2]
assert sorter([1, 2]) == [1, 2]
# Test with sorted lists
def test_sorter_sorted_list():
assert sorter([1, 2, 3, 4, 5]) == [1, 2, 3, 4, 5]
assert sorter([0, 2, 4, 6, 8, 10]) == [0, 2, 4, 6, 8, 10]
# Test with reverse-sorted lists
def test_sorter_reverse_sorted_list():
assert sorter([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert sorter([-1, -2, -3, -4, -5]) == [-5, -4, -3, -2, -1]
# Test with lists with duplicates
def test_sorter_with_duplicates():
assert sorter([3, 1, 2, 1, 3]) == [1, 1, 2, 3, 3]
assert sorter([5, 5, 5, 5]) == [5, 5, 5, 5]
# Test with lists with negative numbers
def test_sorter_with_negative_numbers():
assert sorter([-1, -3, -2, 0, 2]) == [-3, -2, -1, 0, 2]
assert sorter([-10, 100, -50, 0]) == [-50, -10, 0, 100]
# Test with lists with varying types of numbers
def test_sorter_with_various_number_types():
assert sorter([1.5, 2.3, 1.1, 2.0, 1.9]) == [1.1, 1.5, 1.9, 2.0, 2.3]
assert sorter([1, 2.0, 3, 4.5]) == [1, 2.0, 3, 4.5]
# Test with large lists
def test_sorter_large_list():
large_list = list(range(1000, 0, -1)) # 1000 to 1 in reverse order
assert sorter(large_list) == sorted(large_list)
# Test with lists with non-numeric values
def test_sorter_non_numeric():
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['apple', 'banana', 'cherry'])
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['a', 'aa', 'aaa'])To test or edit this optimization locally git merge codeflash/optimize-pr248-2025-03-25T15.14.28
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
There was a problem hiding this comment.
⚡️ Codeflash found optimizations for this PR
📄 4211531.56 (42115.32) speedup for sorter in code_to_optimize/bubble_sort.py
⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)
📝 Explanation and details
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
def sorter(arr):
return sorted(arr)Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | ✅ 39 Passed |
| 🌀 Generated Regression Tests | ✅ 38 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | undefined |
⚙️ Existing Unit Tests Details
- test_bubble_sort.py
- test_bubble_sort__perfinstrumented.py
- test_bubble_sort_conditional.py
- test_bubble_sort_conditional__perfinstrumented.py
- test_bubble_sort_import.py
- test_bubble_sort_import__perfinstrumented.py
- test_bubble_sort_in_class.py
- test_bubble_sort_in_class__perfinstrumented.py
- test_bubble_sort_parametrized.py
- test_bubble_sort_parametrized__perfinstrumented.py
- test_bubble_sort_parametrized_loop.py
- test_bubble_sort_parametrized_loop__perfinstrumented.py
- test_sorter__unit_test_0.py
- test_sorter__unit_test_1.py🌀 Generated Regression Tests Details
# imports
import pytest # used for our unit tests
# function to test
def sorter(arr):
for i in range(len(arr)):
for j in range(len(arr) - 1):
if arr[j] > arr[j + 1]:
temp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = temp
return arr
# unit tests
# Test with an empty list
def test_sorter_empty():
assert sorter([]) == []
# Test with a single-element list
def test_sorter_single_element():
assert sorter([42]) == [42]
# Test with a two-element list
def test_sorter_two_elements():
assert sorter([2, 1]) == [1, 2]
assert sorter([1, 2]) == [1, 2]
# Test with sorted lists
def test_sorter_sorted_list():
assert sorter([1, 2, 3, 4, 5]) == [1, 2, 3, 4, 5]
assert sorter([0, 2, 4, 6, 8, 10]) == [0, 2, 4, 6, 8, 10]
# Test with reverse-sorted lists
def test_sorter_reverse_sorted_list():
assert sorter([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert sorter([-1, -2, -3, -4, -5]) == [-5, -4, -3, -2, -1]
# Test with lists with duplicates
def test_sorter_with_duplicates():
assert sorter([3, 1, 2, 1, 3]) == [1, 1, 2, 3, 3]
assert sorter([5, 5, 5, 5]) == [5, 5, 5, 5]
# Test with lists with negative numbers
def test_sorter_with_negative_numbers():
assert sorter([-1, -3, -2, 0, 2]) == [-3, -2, -1, 0, 2]
assert sorter([-10, 100, -50, 0]) == [-50, -10, 0, 100]
# Test with lists with varying types of numbers
def test_sorter_with_various_number_types():
assert sorter([1.5, 2.3, 1.1, 2.0, 1.9]) == [1.1, 1.5, 1.9, 2.0, 2.3]
assert sorter([1, 2.0, 3, 4.5]) == [1, 2.0, 3, 4.5]
# Test with large lists
def test_sorter_large_list():
large_list = list(range(1000, 0, -1)) # 1000 to 1 in reverse order
assert sorter(large_list) == sorted(large_list)
# Test with lists with non-numeric values
def test_sorter_non_numeric():
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['apple', 'banana', 'cherry'])
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['a', 'aa', 'aaa'])To test or edit this optimization locally git merge codeflash/optimize-pr248-2025-03-25T15.22.41
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
There was a problem hiding this comment.
⚡️ Codeflash found optimizations for this PR
📄 4211531.56 (42115.32) speedup for sorter in code_to_optimize/bubble_sort.py
⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)
📝 Explanation and details
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
def sorter(arr):
return sorted(arr)Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | ✅ 39 Passed |
| 🌀 Generated Regression Tests | ✅ 38 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | undefined |
⚙️ Existing Unit Tests Details
- test_bubble_sort.py
- test_bubble_sort__perfinstrumented.py
- test_bubble_sort_conditional.py
- test_bubble_sort_conditional__perfinstrumented.py
- test_bubble_sort_import.py
- test_bubble_sort_import__perfinstrumented.py
- test_bubble_sort_in_class.py
- test_bubble_sort_in_class__perfinstrumented.py
- test_bubble_sort_parametrized.py
- test_bubble_sort_parametrized__perfinstrumented.py
- test_bubble_sort_parametrized_loop.py
- test_bubble_sort_parametrized_loop__perfinstrumented.py
- test_sorter__unit_test_0.py
- test_sorter__unit_test_1.py🌀 Generated Regression Tests Details
# imports
import pytest # used for our unit tests
# function to test
def sorter(arr):
for i in range(len(arr)):
for j in range(len(arr) - 1):
if arr[j] > arr[j + 1]:
temp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = temp
return arr
# unit tests
# Test with an empty list
def test_sorter_empty():
assert sorter([]) == []
# Test with a single-element list
def test_sorter_single_element():
assert sorter([42]) == [42]
# Test with a two-element list
def test_sorter_two_elements():
assert sorter([2, 1]) == [1, 2]
assert sorter([1, 2]) == [1, 2]
# Test with sorted lists
def test_sorter_sorted_list():
assert sorter([1, 2, 3, 4, 5]) == [1, 2, 3, 4, 5]
assert sorter([0, 2, 4, 6, 8, 10]) == [0, 2, 4, 6, 8, 10]
# Test with reverse-sorted lists
def test_sorter_reverse_sorted_list():
assert sorter([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert sorter([-1, -2, -3, -4, -5]) == [-5, -4, -3, -2, -1]
# Test with lists with duplicates
def test_sorter_with_duplicates():
assert sorter([3, 1, 2, 1, 3]) == [1, 1, 2, 3, 3]
assert sorter([5, 5, 5, 5]) == [5, 5, 5, 5]
# Test with lists with negative numbers
def test_sorter_with_negative_numbers():
assert sorter([-1, -3, -2, 0, 2]) == [-3, -2, -1, 0, 2]
assert sorter([-10, 100, -50, 0]) == [-50, -10, 0, 100]
# Test with lists with varying types of numbers
def test_sorter_with_various_number_types():
assert sorter([1.5, 2.3, 1.1, 2.0, 1.9]) == [1.1, 1.5, 1.9, 2.0, 2.3]
assert sorter([1, 2.0, 3, 4.5]) == [1, 2.0, 3, 4.5]
# Test with large lists
def test_sorter_large_list():
large_list = list(range(1000, 0, -1)) # 1000 to 1 in reverse order
assert sorter(large_list) == sorted(large_list)
# Test with lists with non-numeric values
def test_sorter_non_numeric():
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['apple', 'banana', 'cherry'])
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['a', 'aa', 'aaa'])To test or edit this optimization locally git merge codeflash/optimize-pr248-2025-03-25T15.23.27
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
There was a problem hiding this comment.
⚡️ Codeflash found optimizations for this PR
📄 4211531.56 (42115.32) speedup for sorter in code_to_optimize/bubble_sort.py
⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)
📝 Explanation and details
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
def sorter(arr):
return sorted(arr)Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | ✅ 39 Passed |
| 🌀 Generated Regression Tests | ✅ 38 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | undefined |
⚙️ Existing Unit Tests Details
- test_bubble_sort.py
- test_bubble_sort__perfinstrumented.py
- test_bubble_sort_conditional.py
- test_bubble_sort_conditional__perfinstrumented.py
- test_bubble_sort_import.py
- test_bubble_sort_import__perfinstrumented.py
- test_bubble_sort_in_class.py
- test_bubble_sort_in_class__perfinstrumented.py
- test_bubble_sort_parametrized.py
- test_bubble_sort_parametrized__perfinstrumented.py
- test_bubble_sort_parametrized_loop.py
- test_bubble_sort_parametrized_loop__perfinstrumented.py
- test_sorter__unit_test_0.py
- test_sorter__unit_test_1.py🌀 Generated Regression Tests Details
# imports
import pytest # used for our unit tests
# function to test
def sorter(arr):
for i in range(len(arr)):
for j in range(len(arr) - 1):
if arr[j] > arr[j + 1]:
temp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = temp
return arr
# unit tests
# Test with an empty list
def test_sorter_empty():
assert sorter([]) == []
# Test with a single-element list
def test_sorter_single_element():
assert sorter([42]) == [42]
# Test with a two-element list
def test_sorter_two_elements():
assert sorter([2, 1]) == [1, 2]
assert sorter([1, 2]) == [1, 2]
# Test with sorted lists
def test_sorter_sorted_list():
assert sorter([1, 2, 3, 4, 5]) == [1, 2, 3, 4, 5]
assert sorter([0, 2, 4, 6, 8, 10]) == [0, 2, 4, 6, 8, 10]
# Test with reverse-sorted lists
def test_sorter_reverse_sorted_list():
assert sorter([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert sorter([-1, -2, -3, -4, -5]) == [-5, -4, -3, -2, -1]
# Test with lists with duplicates
def test_sorter_with_duplicates():
assert sorter([3, 1, 2, 1, 3]) == [1, 1, 2, 3, 3]
assert sorter([5, 5, 5, 5]) == [5, 5, 5, 5]
# Test with lists with negative numbers
def test_sorter_with_negative_numbers():
assert sorter([-1, -3, -2, 0, 2]) == [-3, -2, -1, 0, 2]
assert sorter([-10, 100, -50, 0]) == [-50, -10, 0, 100]
# Test with lists with varying types of numbers
def test_sorter_with_various_number_types():
assert sorter([1.5, 2.3, 1.1, 2.0, 1.9]) == [1.1, 1.5, 1.9, 2.0, 2.3]
assert sorter([1, 2.0, 3, 4.5]) == [1, 2.0, 3, 4.5]
# Test with large lists
def test_sorter_large_list():
large_list = list(range(1000, 0, -1)) # 1000 to 1 in reverse order
assert sorter(large_list) == sorted(large_list)
# Test with lists with non-numeric values
def test_sorter_non_numeric():
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['apple', 'banana', 'cherry'])
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['a', 'aa', 'aaa'])To test or edit this optimization locally git merge codeflash/optimize-pr248-2025-03-25T15.30.15
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
⚡️ Codeflash found optimizations for this PR📄 4211531.56 (42115.32) speedup for
|
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
⚡️ Codeflash found optimizations for this PR📄 4211531.56 (42115.32) speedup for
|
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
⚡️ Codeflash found optimizations for this PR📄 4211531.56 (42115.32) speedup for
|
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
⚡️ Codeflash found optimizations for this PR📄 4211531.56 (42115.32) speedup for
|
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
⚡️ Codeflash found optimizations for this PR📄 4211531.56 (42115.32) speedup for
|
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
⚡️ Codeflash found optimizations for this PR📄 4211531.56 (42115.32) speedup for
|
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
⚡️ Codeflash found optimizations for this PR📄 4211531.56 (42115.32) speedup for
|
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
⚡️ Codeflash found optimizations for this PR📄 4211531.56 (42115.32) speedup for
|
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
⚡️ Codeflash found optimizations for this PR📄 4211531.56 (42115.32) speedup for
|
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
⚡️ Codeflash found optimizations for this PR📄 4211531.56 (42115.32) speedup for
|
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
There was a problem hiding this comment.
⚡️ Codeflash found optimizations for this PR
📄 4211531.56 (42115.32) speedup for sorter in code_to_optimize/bubble_sort.py
⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)
📝 Explanation and details
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
def sorter(arr):
return sorted(arr)Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | ✅ 39 Passed |
| 🌀 Generated Regression Tests | ✅ 38 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | undefined |
⚙️ Existing Unit Tests Details
- test_bubble_sort.py
- test_bubble_sort__perfinstrumented.py
- test_bubble_sort_conditional.py
- test_bubble_sort_conditional__perfinstrumented.py
- test_bubble_sort_import.py
- test_bubble_sort_import__perfinstrumented.py
- test_bubble_sort_in_class.py
- test_bubble_sort_in_class__perfinstrumented.py
- test_bubble_sort_parametrized.py
- test_bubble_sort_parametrized__perfinstrumented.py
- test_bubble_sort_parametrized_loop.py
- test_bubble_sort_parametrized_loop__perfinstrumented.py
- test_sorter__unit_test_0.py
- test_sorter__unit_test_1.py🌀 Generated Regression Tests Details
# imports
import pytest # used for our unit tests
# function to test
def sorter(arr):
for i in range(len(arr)):
for j in range(len(arr) - 1):
if arr[j] > arr[j + 1]:
temp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = temp
return arr
# unit tests
# Test with an empty list
def test_sorter_empty():
assert sorter([]) == []
# Test with a single-element list
def test_sorter_single_element():
assert sorter([42]) == [42]
# Test with a two-element list
def test_sorter_two_elements():
assert sorter([2, 1]) == [1, 2]
assert sorter([1, 2]) == [1, 2]
# Test with sorted lists
def test_sorter_sorted_list():
assert sorter([1, 2, 3, 4, 5]) == [1, 2, 3, 4, 5]
assert sorter([0, 2, 4, 6, 8, 10]) == [0, 2, 4, 6, 8, 10]
# Test with reverse-sorted lists
def test_sorter_reverse_sorted_list():
assert sorter([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert sorter([-1, -2, -3, -4, -5]) == [-5, -4, -3, -2, -1]
# Test with lists with duplicates
def test_sorter_with_duplicates():
assert sorter([3, 1, 2, 1, 3]) == [1, 1, 2, 3, 3]
assert sorter([5, 5, 5, 5]) == [5, 5, 5, 5]
# Test with lists with negative numbers
def test_sorter_with_negative_numbers():
assert sorter([-1, -3, -2, 0, 2]) == [-3, -2, -1, 0, 2]
assert sorter([-10, 100, -50, 0]) == [-50, -10, 0, 100]
# Test with lists with varying types of numbers
def test_sorter_with_various_number_types():
assert sorter([1.5, 2.3, 1.1, 2.0, 1.9]) == [1.1, 1.5, 1.9, 2.0, 2.3]
assert sorter([1, 2.0, 3, 4.5]) == [1, 2.0, 3, 4.5]
# Test with large lists
def test_sorter_large_list():
large_list = list(range(1000, 0, -1)) # 1000 to 1 in reverse order
assert sorter(large_list) == sorted(large_list)
# Test with lists with non-numeric values
def test_sorter_non_numeric():
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['apple', 'banana', 'cherry'])
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['a', 'aa', 'aaa'])To test or edit this optimization locally git merge codeflash/optimize-pr248-2025-03-25T17.41.16
| for i in range(len(arr)): | ||
| for j in range(len(arr) - 1): | ||
| if arr[j] > arr[j + 1]: | ||
| if arr[j] > arr[j - 1]: |
There was a problem hiding this comment.
Suggested change
| if arr[j] > arr[j - 1]: | |
| if arr[j] > arr[j + 1]: |
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
| for i in range(len(arr)): | ||
| for j in range(len(arr) - 1): | ||
| if arr[j] > arr[j + 1]: | ||
| if arr[j] > arr[j - 1]: |
There was a problem hiding this comment.
⚡️ Codeflash found optimizations for this PR
📄 4211531.56 (42115.32) speedup for sorter in code_to_optimize/bubble_sort.py
⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)
📝 Explanation and details
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
def sorter(arr):
return sorted(arr)Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | ✅ 39 Passed |
| 🌀 Generated Regression Tests | ✅ 38 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | undefined |
⚙️ Existing Unit Tests Details
- test_bubble_sort.py
- test_bubble_sort__perfinstrumented.py
- test_bubble_sort_conditional.py
- test_bubble_sort_conditional__perfinstrumented.py
- test_bubble_sort_import.py
- test_bubble_sort_import__perfinstrumented.py
- test_bubble_sort_in_class.py
- test_bubble_sort_in_class__perfinstrumented.py
- test_bubble_sort_parametrized.py
- test_bubble_sort_parametrized__perfinstrumented.py
- test_bubble_sort_parametrized_loop.py
- test_bubble_sort_parametrized_loop__perfinstrumented.py
- test_sorter__unit_test_0.py
- test_sorter__unit_test_1.py🌀 Generated Regression Tests Details
# imports
import pytest # used for our unit tests
# function to test
def sorter(arr):
for i in range(len(arr)):
for j in range(len(arr) - 1):
if arr[j] > arr[j + 1]:
temp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = temp
return arr
# unit tests
# Test with an empty list
def test_sorter_empty():
assert sorter([]) == []
# Test with a single-element list
def test_sorter_single_element():
assert sorter([42]) == [42]
# Test with a two-element list
def test_sorter_two_elements():
assert sorter([2, 1]) == [1, 2]
assert sorter([1, 2]) == [1, 2]
# Test with sorted lists
def test_sorter_sorted_list():
assert sorter([1, 2, 3, 4, 5]) == [1, 2, 3, 4, 5]
assert sorter([0, 2, 4, 6, 8, 10]) == [0, 2, 4, 6, 8, 10]
# Test with reverse-sorted lists
def test_sorter_reverse_sorted_list():
assert sorter([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert sorter([-1, -2, -3, -4, -5]) == [-5, -4, -3, -2, -1]
# Test with lists with duplicates
def test_sorter_with_duplicates():
assert sorter([3, 1, 2, 1, 3]) == [1, 1, 2, 3, 3]
assert sorter([5, 5, 5, 5]) == [5, 5, 5, 5]
# Test with lists with negative numbers
def test_sorter_with_negative_numbers():
assert sorter([-1, -3, -2, 0, 2]) == [-3, -2, -1, 0, 2]
assert sorter([-10, 100, -50, 0]) == [-50, -10, 0, 100]
# Test with lists with varying types of numbers
def test_sorter_with_various_number_types():
assert sorter([1.5, 2.3, 1.1, 2.0, 1.9]) == [1.1, 1.5, 1.9, 2.0, 2.3]
assert sorter([1, 2.0, 3, 4.5]) == [1, 2.0, 3, 4.5]
# Test with large lists
def test_sorter_large_list():
large_list = list(range(1000, 0, -1)) # 1000 to 1 in reverse order
assert sorter(large_list) == sorted(large_list)
# Test with lists with non-numeric values
def test_sorter_non_numeric():
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['apple', 'banana', 'cherry'])
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['a', 'aa', 'aaa'])To test or edit this optimization locally git merge codeflash/optimize-pr248-2025-03-25T18.39.05
Suggested change
| if arr[j] > arr[j - 1]: | |
| if arr[j] > arr[j + 1]: |
Saga4
reviewed
Mar 25, 2025
| @@ -1,9 +1,8 @@ | |||
| def sorter(arr): | |||
There was a problem hiding this comment.
Suggested change
| def sorter(arr): | |
| if arr[j] > arr[j + 1]: |
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
| for i in range(len(arr)): | ||
| for j in range(len(arr) - 1): | ||
| if arr[j] > arr[j + 1]: | ||
| if arr[j] > arr[j - 1]: |
There was a problem hiding this comment.
📄 4211531.56 (42115.32) speedup for sorter in code_to_optimize/bubble_sort.py
⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)
📝 Explanation and details
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
def sorter(arr):
return sorted(arr)Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | ✅ 39 Passed |
| 🌀 Generated Regression Tests | ✅ 38 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | undefined |
⚙️ Existing Unit Tests Details
- test_bubble_sort.py
- test_bubble_sort__perfinstrumented.py
- test_bubble_sort_conditional.py
- test_bubble_sort_conditional__perfinstrumented.py
- test_bubble_sort_import.py
- test_bubble_sort_import__perfinstrumented.py
- test_bubble_sort_in_class.py
- test_bubble_sort_in_class__perfinstrumented.py
- test_bubble_sort_parametrized.py
- test_bubble_sort_parametrized__perfinstrumented.py
- test_bubble_sort_parametrized_loop.py
- test_bubble_sort_parametrized_loop__perfinstrumented.py
- test_sorter__unit_test_0.py
- test_sorter__unit_test_1.py🌀 Generated Regression Tests Details
# imports
import pytest # used for our unit tests
# function to test
def sorter(arr):
for i in range(len(arr)):
for j in range(len(arr) - 1):
if arr[j] > arr[j + 1]:
temp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = temp
return arr
# unit tests
# Test with an empty list
def test_sorter_empty():
assert sorter([]) == []
# Test with a single-element list
def test_sorter_single_element():
assert sorter([42]) == [42]
# Test with a two-element list
def test_sorter_two_elements():
assert sorter([2, 1]) == [1, 2]
assert sorter([1, 2]) == [1, 2]
# Test with sorted lists
def test_sorter_sorted_list():
assert sorter([1, 2, 3, 4, 5]) == [1, 2, 3, 4, 5]
assert sorter([0, 2, 4, 6, 8, 10]) == [0, 2, 4, 6, 8, 10]
# Test with reverse-sorted lists
def test_sorter_reverse_sorted_list():
assert sorter([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert sorter([-1, -2, -3, -4, -5]) == [-5, -4, -3, -2, -1]
# Test with lists with duplicates
def test_sorter_with_duplicates():
assert sorter([3, 1, 2, 1, 3]) == [1, 1, 2, 3, 3]
assert sorter([5, 5, 5, 5]) == [5, 5, 5, 5]
# Test with lists with negative numbers
def test_sorter_with_negative_numbers():
assert sorter([-1, -3, -2, 0, 2]) == [-3, -2, -1, 0, 2]
assert sorter([-10, 100, -50, 0]) == [-50, -10, 0, 100]
# Test with lists with varying types of numbers
def test_sorter_with_various_number_types():
assert sorter([1.5, 2.3, 1.1, 2.0, 1.9]) == [1.1, 1.5, 1.9, 2.0, 2.3]
assert sorter([1, 2.0, 3, 4.5]) == [1, 2.0, 3, 4.5]
# Test with large lists
def test_sorter_large_list():
large_list = list(range(1000, 0, -1)) # 1000 to 1 in reverse order
assert sorter(large_list) == sorted(large_list)
# Test with lists with non-numeric values
def test_sorter_non_numeric():
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['apple', 'banana', 'cherry'])
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['a', 'aa', 'aaa'])To test or edit this optimization locally git merge codeflash/optimize-pr248-2025-03-25T21.02.43
Suggested change
| if arr[j] > arr[j - 1]: | |
| if arr[j] > arr[j + 1]: |
codeflash-ai-dev bot
added a commit
that referenced
this pull request
Mar 25, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
| for i in range(len(arr)): | ||
| for j in range(len(arr) - 1): | ||
| if arr[j] > arr[j + 1]: | ||
| if arr[j] > arr[j - 1]: |
There was a problem hiding this comment.
⚡️Codeflash found 4211531.56 (42115.32x) speedup for sorter
⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)
📝 Explanation and details
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
def sorter(arr):
return sorted(arr)Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | ✅ 39 Passed |
| 🌀 Generated Regression Tests | ✅ 38 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | undefined |
⚙️ Existing Unit Tests Details
- test_bubble_sort.py
- test_bubble_sort__perfinstrumented.py
- test_bubble_sort_conditional.py
- test_bubble_sort_conditional__perfinstrumented.py
- test_bubble_sort_import.py
- test_bubble_sort_import__perfinstrumented.py
- test_bubble_sort_in_class.py
- test_bubble_sort_in_class__perfinstrumented.py
- test_bubble_sort_parametrized.py
- test_bubble_sort_parametrized__perfinstrumented.py
- test_bubble_sort_parametrized_loop.py
- test_bubble_sort_parametrized_loop__perfinstrumented.py
- test_sorter__unit_test_0.py
- test_sorter__unit_test_1.py🌀 Generated Regression Tests Details
# imports
import pytest # used for our unit tests
# function to test
def sorter(arr):
for i in range(len(arr)):
for j in range(len(arr) - 1):
if arr[j] > arr[j + 1]:
temp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = temp
return arr
# unit tests
# Test with an empty list
def test_sorter_empty():
assert sorter([]) == []
# Test with a single-element list
def test_sorter_single_element():
assert sorter([42]) == [42]
# Test with a two-element list
def test_sorter_two_elements():
assert sorter([2, 1]) == [1, 2]
assert sorter([1, 2]) == [1, 2]
# Test with sorted lists
def test_sorter_sorted_list():
assert sorter([1, 2, 3, 4, 5]) == [1, 2, 3, 4, 5]
assert sorter([0, 2, 4, 6, 8, 10]) == [0, 2, 4, 6, 8, 10]
# Test with reverse-sorted lists
def test_sorter_reverse_sorted_list():
assert sorter([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert sorter([-1, -2, -3, -4, -5]) == [-5, -4, -3, -2, -1]
# Test with lists with duplicates
def test_sorter_with_duplicates():
assert sorter([3, 1, 2, 1, 3]) == [1, 1, 2, 3, 3]
assert sorter([5, 5, 5, 5]) == [5, 5, 5, 5]
# Test with lists with negative numbers
def test_sorter_with_negative_numbers():
assert sorter([-1, -3, -2, 0, 2]) == [-3, -2, -1, 0, 2]
assert sorter([-10, 100, -50, 0]) == [-50, -10, 0, 100]
# Test with lists with varying types of numbers
def test_sorter_with_various_number_types():
assert sorter([1.5, 2.3, 1.1, 2.0, 1.9]) == [1.1, 1.5, 1.9, 2.0, 2.3]
assert sorter([1, 2.0, 3, 4.5]) == [1, 2.0, 3, 4.5]
# Test with large lists
def test_sorter_large_list():
large_list = list(range(1000, 0, -1)) # 1000 to 1 in reverse order
assert sorter(large_list) == sorted(large_list)
# Test with lists with non-numeric values
def test_sorter_non_numeric():
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['apple', 'banana', 'cherry'])
with pytest.raises(TypeError): # We expect a TypeError because sorter is not designed for non-numeric values
sorter(['a', 'aa', 'aaa'])To test or edit this optimization locally git merge codeflash/optimize-pr248-2025-03-25T21.42.06
Suggested change
| if arr[j] > arr[j - 1]: | |
| if arr[j] > arr[j + 1]: |
codeflash-ai-dev bot
added a commit
that referenced
this pull request
May 14, 2025
…timize-pr107-2025-03-25T14.51.04`)
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
```python
def sorter(arr):
return sorted(arr)
```
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
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⚡️ This pull request contains optimizations for PR #107
If you approve this dependent PR, these changes will be merged into the original PR branch
temp.📄 4211531.56 (42115.32) speedup for
sorterincode_to_optimize/bubble_sort.py⏱️ Runtime :
1070554.63→25.42(best ofundefinedruns)📝 Explanation and details
The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.
However, if you want to achieve a marginal speed increase, writing this in-place might help.
Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:
Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:
Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).
✅ Correctness verification report:
⚙️ Existing Unit Tests Details
🌀 Generated Regression Tests Details
To edit these changes
git checkout codeflash/optimize-pr107-2025-03-25T14.51.04and push.