⚡️ Speed up function funcA by 4,150%

[codeflash-ai]

📄 4,150% (41.50x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 53.9 milliseconds → 1.27 milliseconds (best of 321 runs)

📝 Explanation and details

Here's an optimized rewrite of your program. I’ve focused on the most time-expensive lines in your profiler.

• Loops like for i in range(number * 100): k += i replaced with the arithmetic formula for sum of consecutive integers.
• Building the return string is much faster with a preallocated list and string .join() than repeated generator use (although for Python 3.6+, " ".join(str(i) for i in ...) is already quite efficient, but the list approach can be measurably faster for large counts).
• sum(range(number)) can also be replaced with the formula.
• All existing comments are preserved.
• No function renaming.

Optimized version.

Notes.

• If memory is extremely tight and number can be very large, the list in join can be changed to a generator (but for up to 1000 it is safe, and list is faster).
• The value of k and j is strictly to maintain the same computation and side-effects; they are not used, as in the original code.

Your program should now perform much faster!
Let me know if you want to see micro-benchmarks or further memory optimization.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 55 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

# --- Basic Test Cases ---

def test_funcA_zero():
    # Test with input 0; should return an empty string
    codeflash_output = funcA(0) # 3.15μs -> 2.01μs (56.2% faster)

def test_funcA_one():
    # Test with input 1; should return "0"
    codeflash_output = funcA(1) # 6.12μs -> 2.34μs (161% faster)

def test_funcA_small_number():
    # Test with a small positive integer
    codeflash_output = funcA(3) # 12.3μs -> 2.65μs (364% faster)

def test_funcA_typical_number():
    # Test with a typical number in the middle of the allowed range
    codeflash_output = funcA(10) # 34.5μs -> 3.16μs (994% faster)

# --- Edge Test Cases ---

def test_funcA_negative():
    # Negative numbers should be treated as range(negative) == empty, so return ""
    codeflash_output = funcA(-5) # 2.79μs -> 1.89μs (47.6% faster)

def test_funcA_large_number_limit():
    # Input at the hard limit (1000)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1000) # 3.34ms -> 70.6μs (4630% faster)

def test_funcA_above_limit():
    # Input above the limit should be capped at 1000
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1500) # 3.36ms -> 70.5μs (4672% faster)

def test_funcA_limit_minus_one():
    # Input just below the cap
    expected = " ".join(str(i) for i in range(999))
    codeflash_output = funcA(999) # 3.30ms -> 70.2μs (4610% faster)

def test_funcA_non_integer_input():
    # Should raise TypeError if input is not an integer
    with pytest.raises(TypeError):
        funcA("100")
    with pytest.raises(TypeError):
        funcA(None)
    with pytest.raises(TypeError):
        funcA(5.5)

def test_funcA_boolean_input():
    # True is 1, False is 0 in Python
    codeflash_output = funcA(True) # 6.56μs -> 2.71μs (142% faster)
    codeflash_output = funcA(False) # 1.74μs -> 1.19μs (46.2% faster)

def test_funcA_large_negative():
    # Large negative number, should return ""
    codeflash_output = funcA(-10000) # 2.87μs -> 2.19μs (30.6% faster)

def test_funcA_minimum_integer():
    # Minimum possible integer (simulate)
    codeflash_output = funcA(-2**63) # 3.33μs -> 2.38μs (39.5% faster)

def test_funcA_maximum_integer():
    # Maximum possible integer (simulate, should be capped at 1000)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(2**63-1) # 3.37ms -> 70.5μs (4675% faster)

# --- Large Scale Test Cases ---

def test_funcA_large_scale_1000():
    # Test with the maximum allowed value (1000)
    codeflash_output = funcA(1000); result = codeflash_output # 3.37ms -> 72.1μs (4573% faster)

def test_funcA_large_scale_999():
    # Test with just under the maximum allowed value (999)
    codeflash_output = funcA(999); result = codeflash_output # 3.34ms -> 71.7μs (4563% faster)

def test_funcA_performance():
    # This is a sanity check to ensure the function runs efficiently for large input
    # (pytest will fail if it takes too long)
    import time
    start = time.time()
    funcA(1000)
    end = time.time()

def test_funcA_output_integrity():
    # Check that all numbers are present and in order for a large input
    n = 500
    codeflash_output = funcA(n); result = codeflash_output # 1.62ms -> 37.8μs (4183% faster)
    numbers = result.split(" ")
    for idx, num in enumerate(numbers):
        pass

# --- Extra Robustness Tests ---

@pytest.mark.parametrize("input_val,expected", [
    (0, ""),
    (1, "0"),
    (2, "0 1"),
    (5, "0 1 2 3 4"),
    (10, "0 1 2 3 4 5 6 7 8 9"),
    (1000, " ".join(str(i) for i in range(1000))),
    (1001, " ".join(str(i) for i in range(1000))),
    (-1, ""),
    (True, "0"),
    (False, ""),
])
def test_funcA_parametrized(input_val, expected):
    # Parametrized test for a range of typical and edge values
    codeflash_output = funcA(input_val) # 2.83μs -> 1.86μs (51.6% faster)
# 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 empty string
    codeflash_output = funcA(0) # 2.85μs -> 1.83μs (55.1% faster)

def test_funcA_one():
    # Test with input 1: should return "0"
    codeflash_output = funcA(1) # 6.13μs -> 2.34μs (162% faster)

def test_funcA_small_positive():
    # Test with small positive integer
    codeflash_output = funcA(3) # 12.3μs -> 2.81μs (338% faster)
    codeflash_output = funcA(5) # 16.2μs -> 1.59μs (919% faster)

def test_funcA_typical():
    # Test with a typical value
    codeflash_output = funcA(10) # 34.4μs -> 3.15μs (994% faster)

# Edge Test Cases

def test_funcA_negative():
    # Test with negative input: should return empty string (since range(negative) is empty)
    codeflash_output = funcA(-5) # 2.88μs -> 2.00μs (43.5% faster)

def test_funcA_large_input_capped():
    # Test input above cap: should cap at 1000
    codeflash_output = funcA(1500); result = codeflash_output # 3.33ms -> 79.8μs (4076% faster)
    # Should be numbers 0 through 999, space-separated
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_exactly_at_cap():
    # Test input exactly at cap: 1000
    codeflash_output = funcA(1000); result = codeflash_output # 3.31ms -> 72.9μs (4442% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_just_below_cap():
    # Test input just below cap: 999
    codeflash_output = funcA(999); result = codeflash_output # 3.33ms -> 72.0μs (4522% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_non_integer_input():
    # Test with float input: should raise TypeError
    with pytest.raises(TypeError):
        funcA(5.5)
    with pytest.raises(TypeError):
        funcA("10")
    with pytest.raises(TypeError):
        funcA(None)

def test_funcA_boolean_input():
    # Test with boolean input: True is 1, False is 0
    codeflash_output = funcA(True) # 6.71μs -> 2.77μs (143% faster)
    codeflash_output = funcA(False) # 1.80μs -> 1.25μs (44.1% faster)

def test_funcA_input_is_list():
    # Test with list input: should raise TypeError
    with pytest.raises(TypeError):
        funcA([5])

def test_funcA_input_is_dict():
    # Test with dict input: should raise TypeError
    with pytest.raises(TypeError):
        funcA({'number': 5})

# Large Scale Test Cases

def test_funcA_large_scale_lower_bound():
    # Test with large but valid input (e.g., 500)
    codeflash_output = funcA(500); result = codeflash_output # 1.59ms -> 38.1μs (4077% faster)
    expected = " ".join(str(i) for i in range(500))

def test_funcA_large_scale_upper_bound():
    # Test with input at upper bound (1000)
    codeflash_output = funcA(1000); result = codeflash_output # 3.35ms -> 78.6μs (4165% faster)
    # Should return string of numbers 0 to 999
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_performance():
    # Test that function completes in reasonable time for large input
    import time
    start = time.time()
    codeflash_output = funcA(1000); result = codeflash_output # 3.34ms -> 72.4μs (4513% faster)
    end = time.time()

# Additional Edge Cases

def test_funcA_input_is_max_int():
    # Test with sys.maxsize: should cap at 1000
    import sys
    codeflash_output = funcA(sys.maxsize); result = codeflash_output # 3.31ms -> 72.2μs (4491% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_input_is_min_int():
    # Test with negative sys.maxsize: should return empty string
    import sys
    codeflash_output = funcA(-sys.maxsize); result = codeflash_output # 3.52μs -> 2.57μs (36.6% faster)

def test_funcA_input_is_zero_string():
    # Test with string '0': should raise TypeError
    with pytest.raises(TypeError):
        funcA("0")

def test_funcA_input_is_float_string():
    # Test with string '5.0': should raise TypeError
    with pytest.raises(TypeError):
        funcA("5.0")

def test_funcA_input_is_empty_string():
    # Test with empty string: should raise TypeError
    with pytest.raises(TypeError):
        funcA("")

def test_funcA_input_is_none():
    # Test with None: should raise TypeError
    with pytest.raises(TypeError):
        funcA(None)
# 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-mcl40ok5 and push.