⚡️ Speed up function funcA by 3,905%

[codeflash-ai]

📄 3,905% (39.05x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 37.3 milliseconds → 932 microseconds (best of 556 runs)

📝 Explanation and details

Let's analyze the performance issues as highlighted by the profiler.

• The for i in range(number * 100): k += i loop takes over 99% of the time.
• The " ".join(str(i) for i in range(number)) uses a generator expression, but for larger number values, repeated string concatenations are costly.
• The sum using sum(range(number)) is much faster than the loop, but can be replaced with a direct formula for further speed.

Let's optimize.

1. Replace the sum loop for i in range(number * 100): k += i with the arithmetic series formula: sum_{i=0}^{n-1} i = n*(n-1)//2.
2. The " ".join(...) part is already efficient. However, since str.join() collections can be much faster on prebuilt lists than generators for larger numbers, let's use a list comprehension there.

Here's your rewritten code, optimized for speed.

Why is it faster?

• The O(N) loop is replaced with O(1) math.
• The " ".join(list) is slightly faster than with a generator for this use.
• All preserved logic and return value.

Comments are updated to reflect optimizations. Existing comments on sum simplification and generator usage have been updated according to the new relevant code sections.

Let me know if you need further memory optimizations (eg. generate directly as iterable for huge numbers, or apply similar changes elsewhere)!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 50 Passed
⏪ Replay Tests	✅ 3 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import pytest  # used for our unit tests
from workload import funcA

# unit tests

# 1. Basic Test Cases

def test_funcA_zero():
    """Test with 0 as input, should return an empty string."""
    codeflash_output = funcA(0) # 1.29μs -> 958ns (34.9% faster)

def test_funcA_one():
    """Test with 1 as input, should return '0'."""
    codeflash_output = funcA(1) # 3.58μs -> 1.08μs (231% faster)

def test_funcA_small_number():
    """Test with a small number."""
    codeflash_output = funcA(5) # 12.2μs -> 1.42μs (759% faster)

def test_funcA_typical_number():
    """Test with a typical number."""
    codeflash_output = funcA(10) # 23.0μs -> 1.67μs (1277% faster)

# 2. Edge Test Cases

def test_funcA_negative_number():
    """Test with a negative number, should return an empty string (range(negative) is empty)."""
    codeflash_output = funcA(-5) # 1.29μs -> 958ns (34.9% faster)

def test_funcA_large_but_under_cap():
    """Test with 999, just under the capping threshold."""
    expected = " ".join(str(i) for i in range(999))
    codeflash_output = funcA(999) # 2.37ms -> 56.7μs (4075% faster)

def test_funcA_at_cap():
    """Test with 1000, should cap at 1000."""
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1000) # 2.38ms -> 56.7μs (4093% faster)

def test_funcA_above_cap():
    """Test with 1500, should cap at 1000."""
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1500) # 2.42ms -> 56.7μs (4164% faster)

def test_funcA_maximum_cap_boundary():
    """Test with a very large number, should cap at 1000."""
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(10**6) # 2.42ms -> 56.8μs (4163% faster)

def test_funcA_float_input():
    """Test with a float input. Should raise TypeError since range(float) is invalid."""
    with pytest.raises(TypeError):
        funcA(5.5)

def test_funcA_string_input():
    """Test with a string input. Should raise TypeError."""
    with pytest.raises(TypeError):
        funcA("10")

def test_funcA_none_input():
    """Test with None as input. Should raise TypeError."""
    with pytest.raises(TypeError):
        funcA(None)

def test_funcA_boolean_input():
    """Test with boolean input. Should treat True as 1, False as 0."""
    codeflash_output = funcA(True) # 3.92μs -> 1.42μs (177% faster)
    codeflash_output = funcA(False) # 1.04μs -> 667ns (56.2% faster)

def test_funcA_list_input():
    """Test with a list as input. Should raise TypeError."""
    with pytest.raises(TypeError):
        funcA([10])

def test_funcA_dict_input():
    """Test with a dict as input. Should raise TypeError."""
    with pytest.raises(TypeError):
        funcA({'number': 5})

# 3. Large Scale Test Cases

def test_funcA_large_scale_500():
    """Test with a large number (500), check output length and format."""
    codeflash_output = funcA(500); result = codeflash_output # 1.11ms -> 30.2μs (3586% faster)
    parts = result.split(" ")

def test_funcA_large_scale_999():
    """Test with a number just below the cap (999)."""
    codeflash_output = funcA(999); result = codeflash_output # 2.36ms -> 58.9μs (3905% faster)
    parts = result.split(" ")

def test_funcA_large_scale_cap():
    """Test with the cap value (1000)."""
    codeflash_output = funcA(1000); result = codeflash_output # 2.37ms -> 57.5μs (4022% faster)
    parts = result.split(" ")

def test_funcA_large_scale_above_cap():
    """Test with a value above the cap (1001), should be capped at 1000."""
    codeflash_output = funcA(1001); result = codeflash_output # 2.35ms -> 57.4μs (3996% faster)
    parts = result.split(" ")

def test_funcA_large_scale_performance():
    """Test that the function runs within reasonable time for the cap value."""
    import time
    start = time.time()
    codeflash_output = funcA(1000); result = codeflash_output # 2.41ms -> 57.6μs (4087% faster)
    end = time.time()
    # Check correctness
    parts = result.split(" ")
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import pytest  # used for our unit tests
from workload import funcA

# unit tests

# -------------------------------
# Basic Test Cases
# -------------------------------

def test_funcA_zero():
    # Test with input 0, should return an empty string
    codeflash_output = funcA(0) # 1.33μs -> 1.00μs (33.4% faster)

def test_funcA_one():
    # Test with input 1, should return "0"
    codeflash_output = funcA(1) # 3.71μs -> 1.12μs (230% faster)

def test_funcA_small_number():
    # Test with a small number, e.g., 5
    codeflash_output = funcA(5) # 11.9μs -> 1.42μs (741% faster)

def test_funcA_ten():
    # Test with input 10
    codeflash_output = funcA(10) # 23.0μs -> 1.71μs (1247% faster)

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

def test_funcA_negative():
    # Negative input: range(negative) is empty, should return ""
    codeflash_output = funcA(-5) # 1.25μs -> 958ns (30.5% faster)

def test_funcA_large_but_below_cap():
    # Input just below the cap (999)
    expected = " ".join(str(i) for i in range(999))
    codeflash_output = funcA(999) # 2.41ms -> 57.1μs (4122% faster)

def test_funcA_at_cap():
    # Input at the cap (1000)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1000) # 2.42ms -> 57.0μs (4145% faster)

def test_funcA_above_cap():
    # Input above the cap (e.g., 1001) should be capped at 1000
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1001) # 2.43ms -> 56.9μs (4171% faster)

def test_funcA_far_above_cap():
    # Input much larger than cap (e.g., 5000) should be capped at 1000
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(5000) # 2.44ms -> 56.7μs (4202% faster)

def test_funcA_non_integer_input():
    # Non-integer input: floats should be truncated by range
    # But function expects an int, so let's see the behavior with float
    with pytest.raises(TypeError):
        funcA(5.5)

def test_funcA_string_input():
    # Non-integer input: string should raise TypeError
    with pytest.raises(TypeError):
        funcA("10")

def test_funcA_none_input():
    # None as input should raise TypeError
    with pytest.raises(TypeError):
        funcA(None)

def test_funcA_boolean_input():
    # Boolean input: True is 1, False is 0 in Python
    codeflash_output = funcA(True) # 3.96μs -> 1.42μs (179% faster)
    codeflash_output = funcA(False) # 1.04μs -> 667ns (56.2% faster)

# -------------------------------
# Large Scale Test Cases
# -------------------------------

def test_funcA_large_input_performance():
    # Test with the largest allowed input (1000)
    # Ensure output is as expected and performance is acceptable
    codeflash_output = funcA(1000); result = codeflash_output # 2.37ms -> 58.2μs (3966% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_large_input_off_by_one():
    # Input just over the cap (1001) should be identical to input at cap (1000)
    codeflash_output = funcA(1001) # 2.39ms -> 57.5μs (4059% faster)

def test_funcA_large_input_near_zero():
    # Input near zero (e.g., 1), should return "0"
    codeflash_output = funcA(1) # 3.62μs -> 1.12μs (222% faster)

def test_funcA_large_negative_input():
    # Large negative input should return empty string
    codeflash_output = funcA(-1000) # 1.33μs -> 1.08μs (23.2% faster)

# -------------------------------
# Additional Robustness Cases
# -------------------------------

@pytest.mark.parametrize("input_val, expected", [
    (2, "0 1"),
    (3, "0 1 2"),
    (4, "0 1 2 3"),
    (7, "0 1 2 3 4 5 6"),
])
def test_funcA_various_small_numbers(input_val, expected):
    # Parametrized test for various small positive numbers
    codeflash_output = funcA(input_val) # 5.50μs -> 1.38μs (300% faster)

@pytest.mark.parametrize("input_val", [0, -1, -10, -999, -10000])
def test_funcA_various_negatives(input_val):
    # Parametrized test for various negative numbers, should always return ""
    codeflash_output = funcA(input_val) # 1.25μs -> 958ns (30.5% faster)

def test_funcA_input_is_max_integer():
    # Input is sys.maxsize, should cap at 1000
    import sys
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(sys.maxsize) # 2.40ms -> 56.8μs (4131% faster)

def test_funcA_input_is_min_integer():
    # Input is -sys.maxsize-1, should return ""
    import sys
    codeflash_output = funcA(-sys.maxsize-1) # 1.88μs -> 1.29μs (45.1% 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-funcA-mcl1m9mx and push.