⚡️ Speed up function funcA by 3,893%

[codeflash-ai]

📄 3,893% (38.93x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 45.7 milliseconds → 1.14 milliseconds (best of 378 runs)

📝 Explanation and details

Let's identify and target the main performance bottlenecks from the line profiler.

Bottlenecks

1. for i in range(number * 100): k += i
   • 99%+ of the function runtime is spent here.
   • This loop is just summing numbers from 0 to (number*100 - 1), i.e. it's arithmetic series summation. This can be replaced with a formula for O(1) computation.
2. return " ".join(str(i) for i in range(number))
   • Making many temporary strings and generator expressions per call of str(i).
   • Use map(str, ...) instead of generator expression. This is slightly faster since str is a built-in and map is optimized.
3. sum(range(number))
   • Can be replaced with a formula as well.

Modified code with comments preserved and changes documented.

What changed:

• Replaced the O(N) summing in both the manual loop and sum with O(1) arithmetic formula.
• Used built-in map for string conversion before joining.

Performance gain:
This will eliminate nearly all of the CPU time from the hot loops, making the running time extremely short except for the join operation, which is now as fast as possible. The output and all variable assignments (k, j, returned string) remain exactly as before.

Let me know if you want precise microbenchmark figures!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 46 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) # 2.94μs -> 2.20μs (33.6% faster)

def test_funcA_one():
    # Test with input 1: should return "0"
    codeflash_output = funcA(1) # 6.11μs -> 2.50μs (144% faster)

def test_funcA_small_number():
    # Test with a small number, e.g., 5
    codeflash_output = funcA(5) # 18.5μs -> 2.92μs (534% faster)

def test_funcA_typical_number():
    # Test with a typical number, e.g., 10
    codeflash_output = funcA(10) # 34.3μs -> 3.30μs (941% faster)

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

def test_funcA_negative_number():
    # Negative input: should return empty string because range(negative) is empty
    codeflash_output = funcA(-10) # 2.79μs -> 2.15μs (29.8% faster)

def test_funcA_large_number_cap():
    # Input greater than 1000: should cap at 1000
    codeflash_output = funcA(1500); output = codeflash_output # 3.37ms -> 88.2μs (3724% faster)
    # The output should be the numbers 0 through 999 (1000 numbers)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_exactly_1000():
    # Input exactly 1000: should return numbers 0 to 999
    codeflash_output = funcA(1000); output = codeflash_output # 3.36ms -> 77.6μs (4232% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_just_below_cap():
    # Input just below the cap (999): should return numbers 0 to 998
    codeflash_output = funcA(999); output = codeflash_output # 3.38ms -> 77.4μs (4268% faster)
    expected = " ".join(str(i) for i in range(999))

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

def test_funcA_float_input():
    # Float input should raise TypeError (since range expects int)
    with pytest.raises(TypeError):
        funcA(5.5)

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

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

def test_funcA_large_scale_500():
    # Test with a large input (500)
    codeflash_output = funcA(500); output = codeflash_output # 1.63ms -> 40.5μs (3917% faster)
    expected = " ".join(str(i) for i in range(500))

def test_funcA_large_scale_999():
    # Test with the largest allowed input under the cap (999)
    codeflash_output = funcA(999); output = codeflash_output # 3.35ms -> 78.1μs (4189% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_large_scale_1000():
    # Test with the maximum allowed input (1000)
    codeflash_output = funcA(1000); output = codeflash_output # 3.35ms -> 76.8μs (4259% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_large_scale_just_over_cap():
    # Test with input just over the cap (1001)
    codeflash_output = funcA(1001); output = codeflash_output # 3.35ms -> 77.3μs (4236% faster)
    expected = " ".join(str(i) for i in range(1000))

# ---------------------------
# Additional Robustness Tests
# ---------------------------

def test_funcA_input_is_bool():
    # Boolean input: True is 1, False is 0 in Python
    codeflash_output = funcA(True) # 6.36μs -> 2.75μs (132% faster)
    codeflash_output = funcA(False) # 1.79μs -> 1.42μs (26.2% faster)

def test_funcA_input_is_large_negative():
    # Large negative input: should return empty string
    codeflash_output = funcA(-99999) # 2.77μs -> 2.40μs (15.9% faster)

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

def test_funcA_input_is_list():
    # List input should raise TypeError
    with pytest.raises(TypeError):
        funcA([10])

def test_funcA_input_is_dict():
    # Dict input should raise TypeError
    with pytest.raises(TypeError):
        funcA({'number': 10})

def test_funcA_input_is_none():
    # None input 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.

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) # 2.90μs -> 2.29μs (26.6% faster)

def test_funcA_one():
    # Test with input 1 (should return "0")
    codeflash_output = funcA(1) # 6.02μs -> 2.40μs (150% faster)

def test_funcA_small_number():
    # Test with small input (should return space-separated numbers from 0 to n-1)
    codeflash_output = funcA(3) # 11.9μs -> 2.75μs (335% faster)
    codeflash_output = funcA(5) # 16.4μs -> 1.57μs (941% faster)

def test_funcA_typical_number():
    # Test with a typical input in the middle range
    n = 10
    expected = " ".join(str(i) for i in range(n))
    codeflash_output = funcA(n) # 33.5μs -> 2.73μs (1129% faster)

# --- Edge Test Cases ---

def test_funcA_negative():
    # Negative input should be treated as range(negative) = empty, so returns ""
    codeflash_output = funcA(-5) # 2.75μs -> 2.17μs (26.3% faster)

def test_funcA_large_but_under_limit():
    # Test with a large input below the 1000 cap
    n = 999
    expected = " ".join(str(i) for i in range(n))
    codeflash_output = funcA(n) # 3.35ms -> 76.6μs (4275% faster)

def test_funcA_at_limit():
    # Test with input exactly at the cap (1000)
    n = 1000
    expected = " ".join(str(i) for i in range(n))
    codeflash_output = funcA(n) # 3.36ms -> 75.9μs (4330% faster)

def test_funcA_above_limit():
    # Test with input above the cap (should cap at 1000)
    n = 1234
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(n) # 3.35ms -> 76.1μs (4308% faster)

def test_funcA_edge_case_one_below_limit():
    # Test with input one below the cap
    n = 999
    expected = " ".join(str(i) for i in range(n))
    codeflash_output = funcA(n) # 3.34ms -> 75.8μs (4299% faster)

def test_funcA_non_integer_input():
    # Non-integer input should raise a TypeError
    with pytest.raises(TypeError):
        funcA("10")
    with pytest.raises(TypeError):
        funcA(5.5)
    with pytest.raises(TypeError):
        funcA(None)

def test_funcA_boolean_input():
    # Booleans are ints in Python: True == 1, False == 0
    codeflash_output = funcA(True) # 6.36μs -> 2.90μs (119% faster)
    codeflash_output = funcA(False) # 1.74μs -> 1.33μs (30.8% faster)

# --- Large Scale Test Cases ---

def test_funcA_large_scale_just_below_cap():
    # Test with a large value just below the cap to assess performance
    n = 999
    codeflash_output = funcA(n); result = codeflash_output # 3.34ms -> 78.6μs (4157% faster)
    # Check length and content
    parts = result.split(" ")

def test_funcA_large_scale_at_cap():
    # Test with the cap value
    n = 1000
    codeflash_output = funcA(n); result = codeflash_output # 3.40ms -> 77.2μs (4308% faster)
    parts = result.split(" ")

def test_funcA_large_scale_above_cap():
    # Test with value above cap; should still return 1000 numbers
    n = 2000
    codeflash_output = funcA(n); result = codeflash_output # 3.37ms -> 77.2μs (4274% faster)
    parts = result.split(" ")

# --- Additional Robustness Tests ---

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

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

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

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

def test_funcA_input_is_float_integer():
    # Float that is an integer should raise TypeError (since range expects int)
    with pytest.raises(TypeError):
        funcA(10.0)

def test_funcA_input_is_complex():
    # Complex number input should raise TypeError
    with pytest.raises(TypeError):
        funcA(3+4j)
# 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-mcl4nstm and push.