⚡️ Speed up function funcA by 3,933%

[codeflash-ai]

📄 3,933% (39.33x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 56.5 milliseconds → 1.40 milliseconds (best of 367 runs)

📝 Explanation and details

Here's an optimized version of your code. The biggest performance improvement comes from removing unnecessary loops and computations. For example, both summations can be computed with formulas, and the string join can be optimized by using map (which is faster and more memory efficient than a generator expression in this context).

Notes:

• Comments kept for the relevant explanations.
• k and j are computed but not used; if they are unneeded, you might consider omitting them entirely. If they are needed in the future, the new forms are mathematically equivalent and much faster.
• The use of map(str, ...) speeds up the join operation compared to a generator expression.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 44 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 for input 0, should return an empty string
    codeflash_output = funcA(0) # 2.90μs -> 2.12μs (36.9% faster)

def test_funcA_one():
    # Test for input 1, should return "0"
    codeflash_output = funcA(1) # 6.31μs -> 2.54μs (149% faster)

def test_funcA_small_number():
    # Test for a small positive integer
    codeflash_output = funcA(5) # 18.6μs -> 3.02μs (515% faster)

def test_funcA_typical_number():
    # Test for a typical number within range
    codeflash_output = funcA(10) # 34.1μs -> 3.28μs (941% faster)

def test_funcA_string_output_format():
    # Test that output is a string and numbers are separated by single spaces
    codeflash_output = funcA(7); result = codeflash_output # 24.5μs -> 2.92μs (739% faster)

# -----------------------
# EDGE TEST CASES
# -----------------------

def test_funcA_negative_number():
    # Negative input should behave like 0 (since range(-n) is empty)
    codeflash_output = funcA(-5) # 2.77μs -> 2.14μs (29.0% faster)

def test_funcA_large_but_below_limit():
    # Input just below the cap
    n = 999
    codeflash_output = funcA(n); output = codeflash_output # 3.34ms -> 79.4μs (4101% faster)

def test_funcA_at_limit():
    # Input at the cap
    n = 1000
    codeflash_output = funcA(n); output = codeflash_output # 3.30ms -> 77.4μs (4162% faster)

def test_funcA_above_limit():
    # Input above the cap should be capped at 1000
    n = 1050
    codeflash_output = funcA(n); output = codeflash_output # 3.31ms -> 77.6μs (4169% faster)

def test_funcA_float_input():
    # Float input should raise TypeError
    with pytest.raises(TypeError):
        funcA(3.5)

def test_funcA_string_input():
    # String input should raise TypeError
    with pytest.raises(TypeError):
        funcA("10")

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

def test_funcA_boolean_input():
    # Boolean input: True is 1, False is 0
    codeflash_output = funcA(True) # 6.69μs -> 3.04μs (121% faster)
    codeflash_output = funcA(False) # 1.84μs -> 1.40μs (31.4% faster)

# -----------------------
# LARGE SCALE TEST CASES
# -----------------------

def test_funcA_large_scale_near_limit():
    # Test with large input near the cap
    n = 999
    codeflash_output = funcA(n); output = codeflash_output # 3.33ms -> 81.5μs (3991% faster)
    numbers = output.split()

def test_funcA_large_scale_at_limit():
    # Test with the maximum allowed input
    n = 1000
    codeflash_output = funcA(n); output = codeflash_output # 3.30ms -> 77.7μs (4150% faster)
    numbers = output.split()

def test_funcA_performance():
    # This test checks that the function doesn't take too long for large input
    import time
    n = 1000
    start = time.time()
    codeflash_output = funcA(n); result = codeflash_output # 3.29ms -> 77.1μs (4167% faster)
    end = time.time()

# -----------------------
# MISCELLANEOUS/ROBUSTNESS
# -----------------------

def test_funcA_does_not_mutate_input():
    # Ensure input variable is not mutated outside
    n = 10
    funcA(n)

def test_funcA_no_trailing_spaces():
    # Output should not have leading or trailing spaces
    codeflash_output = funcA(15); result = codeflash_output # 50.0μs -> 3.69μs (1256% faster)

def test_funcA_single_space_separation():
    # There should be only single spaces between numbers
    codeflash_output = funcA(20); result = codeflash_output # 65.7μs -> 3.97μs (1555% faster)

def test_funcA_output_for_large_negative():
    # Large negative input should still return empty string
    codeflash_output = funcA(-99999) # 2.90μs -> 2.35μs (23.0% 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_zero():
    # Test with number = 0 (should return empty string)
    codeflash_output = funcA(0) # 3.02μs -> 2.23μs (35.4% faster)

def test_one():
    # Test with number = 1 (should return "0")
    codeflash_output = funcA(1) # 6.20μs -> 2.60μs (138% faster)

def test_small_number():
    # Test with number = 5
    codeflash_output = funcA(5) # 18.5μs -> 3.00μs (518% faster)

def test_typical_small():
    # Test with number = 10
    codeflash_output = funcA(10) # 34.2μs -> 3.32μs (933% faster)

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

def test_negative_number():
    # Negative numbers should behave like range(negative) = empty
    codeflash_output = funcA(-5) # 2.73μs -> 2.20μs (23.6% faster)

def test_number_is_1000():
    # Should return string from 0 to 999, separated by spaces
    codeflash_output = funcA(1000); result = codeflash_output # 3.35ms -> 79.7μs (4102% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_number_above_1000():
    # Should cap at 1000
    codeflash_output = funcA(1500); result = codeflash_output # 3.35ms -> 77.3μs (4237% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_number_is_999():
    # Should return string from 0 to 998
    codeflash_output = funcA(999); result = codeflash_output # 3.34ms -> 77.1μs (4234% faster)
    expected = " ".join(str(i) for i in range(999))

def test_number_is_none():
    # Should raise TypeError if None is passed
    with pytest.raises(TypeError):
        funcA(None)

def test_number_is_float():
    # Should raise TypeError if float is passed (since range expects int)
    with pytest.raises(TypeError):
        funcA(2.5)

def test_number_is_string():
    # Should raise TypeError if string is passed
    with pytest.raises(TypeError):
        funcA("10")

def test_number_is_bool():
    # True is 1, so should behave as funcA(1)
    codeflash_output = funcA(True) # 6.81μs -> 3.04μs (124% faster)
    # False is 0, so should behave as funcA(0)
    codeflash_output = funcA(False) # 1.69μs -> 1.43μs (18.1% faster)

def test_number_is_large_negative():
    # Large negative should return empty string
    codeflash_output = funcA(-99999) # 2.75μs -> 2.48μs (10.5% faster)

def test_number_is_max_int():
    # Should cap at 1000
    import sys
    codeflash_output = funcA(sys.maxsize); result = codeflash_output # 3.37ms -> 80.1μs (4112% faster)
    expected = " ".join(str(i) for i in range(1000))

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

def test_large_scale_at_cap():
    # Test for upper cap (1000)
    codeflash_output = funcA(1000); result = codeflash_output # 3.35ms -> 77.8μs (4212% faster)
    # Check first and last values
    split_result = result.split()

def test_large_scale_just_below_cap():
    # Test for just below the cap (999)
    codeflash_output = funcA(999); result = codeflash_output # 3.34ms -> 77.3μs (4220% faster)
    split_result = result.split()

def test_large_scale_above_cap():
    # Test for a value above the cap (should still return 1000 values)
    codeflash_output = funcA(5000); result = codeflash_output # 3.35ms -> 77.3μs (4233% faster)
    split_result = result.split()

def test_large_scale_random_within_cap():
    # Test with a random number within the cap
    import random
    n = random.randint(500, 999)
    codeflash_output = funcA(n); result = codeflash_output # 2.74ms -> 64.3μs (4156% faster)
    split_result = result.split()

def test_performance_large():
    # Test that the function completes quickly for n=1000
    import time
    start = time.time()
    funcA(1000)
    elapsed = time.time() - start

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

def test_number_is_min_int():
    # Should return empty string for very large negative number
    import sys
    codeflash_output = funcA(-sys.maxsize) # 3.91μs -> 2.85μs (36.8% faster)

def test_number_is_exactly_cap():
    # Should return 1000 numbers
    codeflash_output = funcA(1000); result = codeflash_output # 3.30ms -> 77.4μs (4156% faster)

def test_number_is_just_below_cap():
    # Should return 999 numbers
    codeflash_output = funcA(999); result = codeflash_output # 3.31ms -> 77.3μs (4176% 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-mcl41rke and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #470 by codeflash-ai[bot] was closed.