⚡️ Speed up function funcA by 4,113%

[codeflash-ai]

📄 4,113% (41.13x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 47.1 milliseconds → 1.12 milliseconds (best of 320 runs)

📝 Explanation and details

Here’s your optimized program, rewritten for maximal speed and reduced memory use.
Key optimizations:

1. Replace O(N) loop for summing with a direct formula (k = sum(range(number * 100)) == (n-1)*n/2): replaces explicit iteration with a pure arithmetic expression—much faster.
2. Use f-string and list comprehension for str join (more efficient than generator expressions in CPython, better than repeated calls).
3. Avoid unnecessary assignments and keep only results relevant for function output if required, but per your request, we must keep the function return unchanged.

Optimized code.

Notes.

• The core bottleneck was the explicit for-loop for summing, replaced by the direct formula.
• " ".join(str(i) for i in range(number)) is very slightly slower than .join([str(i) for i in range(number)]) in most versions of CPython for large numbers, due to generator overhead.
• Memory use for join is O(n) in all cases, but the rest of the function is now minimal.

This should greatly reduce the runtime (from hundreds of ms to a small fraction), as almost all the time was being spent in the explicit for-loops.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 51 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) # 2.98μs -> 1.91μs (55.4% faster)

def test_funcA_one():
    # Test with input 1: should return "0"
    codeflash_output = funcA(1) # 6.28μs -> 2.38μs (163% faster)

def test_funcA_two():
    # Test with input 2: should return "0 1"
    codeflash_output = funcA(2) # 8.76μs -> 2.48μs (252% faster)

def test_funcA_small_number():
    # Test with small number, e.g., 5
    codeflash_output = funcA(5) # 18.8μs -> 2.98μs (528% faster)

def test_funcA_ten():
    # Test with input 10
    codeflash_output = funcA(10) # 34.5μs -> 3.26μs (959% faster)

# --- Edge Test Cases ---

def test_funcA_negative():
    # Test with negative input: should behave like input 0, i.e., return ""
    codeflash_output = funcA(-5) # 2.73μs -> 1.96μs (38.7% faster)

def test_funcA_large_input():
    # Test with input much larger than 1000, should cap at 1000
    codeflash_output = funcA(5000); result = codeflash_output # 3.43ms -> 83.4μs (4009% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_exactly_1000():
    # Test with input exactly 1000, should return "0 1 ... 999"
    codeflash_output = funcA(1000); result = codeflash_output # 3.41ms -> 71.9μs (4641% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_just_below_1000():
    # Test with input 999, should return "0 1 ... 998"
    codeflash_output = funcA(999); result = codeflash_output # 3.37ms -> 72.0μs (4584% 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(3.5)

def test_funcA_string_input():
    # Test with 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: True is 1, False is 0
    codeflash_output = funcA(True) # 6.69μs -> 2.85μs (135% faster)
    codeflash_output = funcA(False) # 1.72μs -> 1.19μs (44.5% faster)

# --- Large Scale Test Cases ---

def test_funcA_large_scale_500():
    # Test with input 500, output should be "0 1 ... 499"
    codeflash_output = funcA(500); result = codeflash_output # 1.61ms -> 37.7μs (4175% faster)
    expected = " ".join(str(i) for i in range(500))

def test_funcA_large_scale_999():
    # Test with input 999, output should be "0 1 ... 998"
    codeflash_output = funcA(999); result = codeflash_output # 3.37ms -> 73.3μs (4493% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_large_scale_performance():
    # Test that function does not hang or error with input 1000 (upper bound)
    codeflash_output = funcA(1000); result = codeflash_output # 3.36ms -> 71.7μs (4588% faster)

# --- Additional Edge Cases ---

def test_funcA_input_is_minimum_integer():
    # Test with minimum integer (simulate very large negative number)
    codeflash_output = funcA(-999999999) # 2.97μs -> 2.40μs (23.8% faster)

def test_funcA_input_is_maximum_integer():
    # Test with maximum integer (simulate very large positive number)
    codeflash_output = funcA(999999999); result = codeflash_output # 3.39ms -> 71.7μs (4624% faster)
    expected = " ".join(str(i) for i in range(1000))

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

def test_funcA_input_is_list():
    # Test with list input, should raise TypeError
    with pytest.raises(TypeError):
        funcA([1,2,3])

def test_funcA_input_is_dict():
    # Test with dict input, should raise TypeError
    with pytest.raises(TypeError):
        funcA({'a': 1})

# --- Mutant Killing Tests (robustness) ---

def test_funcA_output_values_are_correct():
    # Ensure output is space-separated and numbers are in order
    n = 20
    codeflash_output = funcA(n); result = codeflash_output # 68.0μs -> 4.32μs (1476% faster)
    parts = result.split(" ")
    for idx, val in enumerate(parts):
        pass

def test_funcA_no_trailing_space():
    # Ensure there is no trailing space
    n = 50
    codeflash_output = funcA(n); result = codeflash_output # 164μs -> 6.17μs (2561% faster)

def test_funcA_no_leading_space():
    # Ensure there is no leading space
    n = 50
    codeflash_output = funcA(n); result = codeflash_output # 163μs -> 6.07μs (2590% 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 an empty string)
    codeflash_output = funcA(0) # 2.77μs -> 1.77μs (56.0% faster)

def test_funcA_one():
    # Test with input 1 (should return just "0")
    codeflash_output = funcA(1) # 6.09μs -> 2.35μs (160% faster)

def test_funcA_small_number():
    # Test with a small number (e.g., 5)
    codeflash_output = funcA(5) # 18.7μs -> 2.94μs (537% faster)

def test_funcA_typical_number():
    # Test with a typical number (e.g., 10)
    codeflash_output = funcA(10) # 34.5μs -> 3.25μs (961% faster)

# --- Edge Test Cases ---

def test_funcA_negative_number():
    # Test with a negative number (should behave like zero, return empty string)
    codeflash_output = funcA(-5) # 2.75μs -> 1.93μs (42.0% faster)

def test_funcA_large_number_cap():
    # Test with a number above the cap (e.g., 1500, should cap at 1000)
    codeflash_output = funcA(1500); result = codeflash_output # 3.37ms -> 73.5μs (4484% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_at_cap():
    # Test with the cap value (1000)
    codeflash_output = funcA(1000); result = codeflash_output # 3.41ms -> 72.2μs (4629% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_just_below_cap():
    # Test with 999 (just below the cap)
    codeflash_output = funcA(999); result = codeflash_output # 3.38ms -> 71.8μs (4603% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_non_integer_input():
    # Test with a float input (should raise TypeError)
    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) # 6.54μs -> 2.88μs (128% faster)
    codeflash_output = funcA(False) # 1.78μs -> 1.25μs (42.4% faster)

def test_funcA_large_negative():
    # Test with a large negative number (should return empty string)
    codeflash_output = funcA(-99999) # 2.69μs -> 2.20μs (22.3% faster)

# --- Large Scale Test Cases ---

def test_funcA_large_scale_100():
    # Test with a reasonably large number (e.g., 100)
    codeflash_output = funcA(100); result = codeflash_output # 329μs -> 10.1μs (3154% faster)
    expected = " ".join(str(i) for i in range(100))

def test_funcA_large_scale_999():
    # Test with 999 (just under the cap)
    codeflash_output = funcA(999); result = codeflash_output # 3.43ms -> 73.7μs (4557% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_large_scale_1000():
    # Test with 1000 (at the cap)
    codeflash_output = funcA(1000); result = codeflash_output # 3.36ms -> 71.9μs (4580% faster)
    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 # 3.41ms -> 71.4μs (4676% faster)
    end = time.time()
    # Also check correctness
    expected = " ".join(str(i) for i in range(1000))

# --- Additional Edge Cases ---

def test_funcA_input_is_maxint():
    # Test with a very large integer (should cap at 1000)
    import sys
    codeflash_output = funcA(sys.maxsize); result = codeflash_output # 3.36ms -> 71.7μs (4587% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_input_is_minint():
    # Test with a very small integer (should return empty string)
    import sys
    codeflash_output = funcA(-sys.maxsize); result = codeflash_output # 3.49μs -> 2.51μs (38.7% 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_list():
    # Test with a list input (should raise TypeError)
    with pytest.raises(TypeError):
        funcA([1,2,3])

def test_funcA_input_is_dict():
    # Test with a dict input (should raise TypeError)
    with pytest.raises(TypeError):
        funcA({'a': 1})

def test_funcA_input_is_float_int_equiv():
    # Test with a float that is an integer value (should raise TypeError)
    with pytest.raises(TypeError):
        funcA(10.0)

# --- Determinism Test ---

def test_funcA_determinism():
    # Test that repeated calls with same input give same output
    codeflash_output = funcA(50); result1 = codeflash_output # 161μs -> 6.51μs (2375% faster)
    codeflash_output = funcA(50); result2 = codeflash_output # 159μs -> 4.79μs (3229% 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-mcl4nqvl and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #473 by codeflash-ai[bot] was closed.