⚡️ Speed up function funcA by 4,036%

[codeflash-ai]

📄 4,036% (40.36x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 39.3 milliseconds → 950 microseconds (best of 669 runs)

📝 Explanation and details

Here is your optimized version of funcA.
Key Optimizations.

• Replaced the explicit for-loop summing with a direct arithmetic formula for the sum of an integer series, eliminating the O(N) loops (sum_ = n*(n-1)//2).
• Kept the sum(range(number)) using the same formula for instant computation.
• For string joining, avoided repeated str(i) conversions by using a list comprehension, which is slightly more efficient than a generator in CPython for large N (since the generator must yield each value and context switch repeatedly, whereas the list allocates the result array once).
• Used local variables and inlined conditions for improved readability and speed.
• No allocating or storing of unused variables.
• All semantics and return values are preserved.

Here is the rewritten, faster code.

Explanations:

• k = (number * 100 - 1) * (number * 100) // 2 computes sum of 0..(number*100-1) instantly.
• j = (number - 1) * number // 2 computes sum of 0..(number-1) instantly.
• return " ".join([str(i) for i in range(number)]): the list comprehension is slightly faster for this use than a generator in most tested CPython versions, though for very small number values, the difference is negligible.

If you still want to squeeze out every drop, replacing the join line with an f-string for the whole sequence isn't worth it for up to 1000 numbers due to memory and performance, so this is optimal for both time and memory.

All output and side effects are identical to your original program.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 41 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_basic_small_number():
    # Test with a small positive integer
    codeflash_output = funcA(5); result = codeflash_output # 12.0μs -> 1.50μs (700% faster)

def test_funcA_basic_zero():
    # Test with zero
    codeflash_output = funcA(0); result = codeflash_output # 1.29μs -> 1.00μs (29.1% faster)

def test_funcA_basic_one():
    # Test with one
    codeflash_output = funcA(1); result = codeflash_output # 3.67μs -> 1.21μs (203% faster)

def test_funcA_basic_typical():
    # Test with a typical small number
    codeflash_output = funcA(10); result = codeflash_output # 23.2μs -> 1.71μs (1259% faster)

# 2. Edge Test Cases

def test_funcA_negative_number():
    # Test with a negative number
    codeflash_output = funcA(-3); result = codeflash_output # 1.25μs -> 1.00μs (25.0% faster)

def test_funcA_large_number_at_limit():
    # Test with the number exactly at the capping threshold
    codeflash_output = funcA(1000); result = codeflash_output # 2.47ms -> 57.9μs (4162% faster)
    # Should return numbers from 0 to 999
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_large_number_above_limit():
    # Test with number above the capping threshold
    codeflash_output = funcA(1500); result = codeflash_output # 2.47ms -> 57.2μs (4222% faster)
    # Should cap at 1000
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_edge_case_just_below_limit():
    # Test with number just below the capping threshold
    codeflash_output = funcA(999); result = codeflash_output # 2.53ms -> 57.0μs (4339% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_edge_case_negative_one():
    # Test with -1, should return empty string
    codeflash_output = funcA(-1); result = codeflash_output # 1.25μs -> 1.00μs (25.0% faster)

def test_funcA_float_input():
    # Test with a float input, should raise TypeError
    with pytest.raises(TypeError):
        funcA(3.5)

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_bool_input():
    # Test with boolean input, which is valid in Python as int (True==1, False==0)
    codeflash_output = funcA(True); result_true = codeflash_output # 3.88μs -> 1.46μs (166% faster)
    codeflash_output = funcA(False); result_false = codeflash_output # 917ns -> 666ns (37.7% faster)

def test_funcA_large_negative():
    # Test with a large negative number
    codeflash_output = funcA(-1000); result = codeflash_output # 1.33μs -> 1.08μs (23.0% faster)

# 3. Large Scale Test Cases

def test_funcA_large_scale_500():
    # Test with a large, but not capped, number
    codeflash_output = funcA(500); result = codeflash_output # 1.13ms -> 29.5μs (3728% faster)
    expected = " ".join(str(i) for i in range(500))

def test_funcA_large_scale_999():
    # Test with the largest uncapped value
    codeflash_output = funcA(999); result = codeflash_output # 2.41ms -> 57.3μs (4115% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_large_scale_performance():
    # Test with the capped value for performance (should not exceed 1000 elements)
    codeflash_output = funcA(1000); result = codeflash_output # 2.43ms -> 57.0μs (4162% faster)
    # Check that the output is not too large and is as expected
    split_result = result.split()

def test_funcA_large_scale_above_cap():
    # Test with a value much larger than the cap
    codeflash_output = funcA(9999); result = codeflash_output # 2.43ms -> 56.9μs (4165% faster)
    split_result = result.split()

def test_funcA_large_scale_type_and_content():
    # Test that the return type is string and content is as expected for a large input
    codeflash_output = funcA(1000); output = codeflash_output # 2.40ms -> 57.1μs (4110% faster)
    # Check a few sample points
    items = output.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) # 1.29μs -> 958ns (34.9% faster)

def test_funcA_one():
    # Test with input 1, should return "0"
    codeflash_output = funcA(1) # 3.67μs -> 1.12μs (226% faster)

def test_funcA_small_number():
    # Test with a small number
    codeflash_output = funcA(5) # 12.2μs -> 1.46μs (734% faster)

def test_funcA_typical_number():
    # Test with a typical number
    codeflash_output = funcA(10) # 23.3μs -> 1.67μs (1298% faster)

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

def test_funcA_negative_number():
    # Test with a negative number, should return an empty string (since range(negative) is empty)
    codeflash_output = funcA(-5) # 1.25μs -> 1.00μs (25.0% faster)

def test_funcA_large_number_limit():
    # Test with number exactly at the limit (1000), should return "0 1 ... 999"
    codeflash_output = funcA(1000); result = codeflash_output # 2.43ms -> 57.6μs (4114% faster)
    parts = result.split()

def test_funcA_above_limit():
    # Test with a number above the limit, should be capped at 1000
    codeflash_output = funcA(1500); result = codeflash_output # 2.43ms -> 57.0μs (4159% faster)
    parts = result.split()

def test_funcA_limit_minus_one():
    # Test with number just below the limit
    codeflash_output = funcA(999); result = codeflash_output # 2.42ms -> 57.0μs (4148% faster)
    parts = result.split()

def test_funcA_non_integer_input():
    # Test with a float input, should raise TypeError
    with pytest.raises(TypeError):
        funcA(2.5)

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 input, should raise TypeError
    with pytest.raises(TypeError):
        funcA(None)

def test_funcA_boolean_input():
    # Test with boolean input, True treated as 1, False as 0
    codeflash_output = funcA(True) # 3.83μs -> 1.42μs (171% faster)
    codeflash_output = funcA(False) # 875ns -> 625ns (40.0% faster)

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

def test_funcA_large_scale_500():
    # Test with a large number below the cap
    n = 500
    codeflash_output = funcA(n); result = codeflash_output # 1.13ms -> 29.7μs (3718% faster)
    parts = result.split()

def test_funcA_large_scale_999():
    # Test with the largest number below the cap
    n = 999
    codeflash_output = funcA(n); result = codeflash_output # 2.47ms -> 57.9μs (4170% faster)
    parts = result.split()

def test_funcA_large_scale_at_cap():
    # Test with the cap value
    n = 1000
    codeflash_output = funcA(n); result = codeflash_output # 2.50ms -> 57.0μs (4289% faster)
    parts = result.split()

def test_funcA_large_scale_above_cap():
    # Test with a number above the cap, should still return 1000 numbers
    n = 2000
    codeflash_output = funcA(n); result = codeflash_output # 2.50ms -> 57.1μs (4285% faster)
    parts = result.split()

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

def test_funcA_minimum_integer():
    # Test with minimum possible integer (simulate extreme negative)
    codeflash_output = funcA(-999999) # 1.29μs -> 1.12μs (14.8% faster)

def test_funcA_zero_string_output():
    # Ensure that for 0, the output is exactly an empty string
    codeflash_output = funcA(0) # 1.29μs -> 959ns (34.6% faster)

def test_funcA_input_is_1000():
    # Ensure that input 1000 produces the correct last number
    codeflash_output = funcA(1000); result = codeflash_output # 2.43ms -> 57.3μs (4137% faster)

def test_funcA_input_is_999():
    # Ensure that input 999 produces the correct last number
    codeflash_output = funcA(999); result = codeflash_output # 2.46ms -> 56.7μs (4235% 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-mcjhp66i and push.