⚡️ Speed up function funcA by 17%

[codeflash-ai]

📄 17% (0.17x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 1.44 milliseconds → 1.23 milliseconds (best of 328 runs)

📝 Explanation and details

Here’s an optimized version of your code. The overwhelming majority of time is spent joining and converting numbers to strings (the line with " ".join(map(str, range(number)))).
Python's core string join and str are already highly optimized, but for further speedup, we can reduce the number of temporary string objects created during joining.
Preallocating and using a list comprehension to convert numbers to strings, and then joining, is marginally faster for large numbers.
On Python 3.6+ (as in your version), using str(i) is reliable, but using f-strings with list comprehensions is slightly faster.
You may also use the array module, but for string conversion, it's generally not faster.
We will also eliminate unnecessary calculations as their results are not used.

Here's the faster version.

Notes on the rewrite.

• The preallocated list comprehension with f-strings is faster than map(str, ...) in this context according to microbenchmarks.
• Removing the unnecessary variable calculations reduces wasted CPU.
• If extremely high speed is required and the input number is always relatively small (up to 1000), you could also precompute all possible outputs and index into the result, at the cost of memory—you can ask for that version if needed.

Summary:
The optimization here is mostly removing dead code and making string conversion and joining as fast as Python allows. For huge scale, alternative approaches (precomputing, C extensions) would be needed, but for your requirements, this is the fastest pure Python code.

Let me know if you want advanced (Cython or memcpy) solutions!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 50 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_zero():
    # Test with input 0: should return an empty string
    codeflash_output = funcA(0) # 2.40μs -> 1.53μs (56.2% faster)

def test_funcA_one():
    # Test with input 1: should return '0'
    codeflash_output = funcA(1) # 2.67μs -> 1.92μs (38.5% faster)

def test_funcA_small_number():
    # Test with input 5: should return '0 1 2 3 4'
    codeflash_output = funcA(5) # 2.96μs -> 2.42μs (22.4% faster)

def test_funcA_typical_number():
    # Test with input 10: should return '0 1 2 3 4 5 6 7 8 9'
    codeflash_output = funcA(10) # 3.36μs -> 2.62μs (27.9% faster)

def test_funcA_typical_number_2():
    # Test with input 15
    codeflash_output = funcA(15) # 3.61μs -> 2.94μs (22.5% faster)


# 2. Edge Test Cases

def test_funcA_negative_number():
    # Negative input should result in an empty string (since range(negative) is empty)
    codeflash_output = funcA(-5) # 2.22μs -> 1.42μs (56.3% faster)

def test_funcA_large_number_cap():
    # Input greater than 1000 should be capped at 1000
    codeflash_output = funcA(1500); result = codeflash_output # 79.7μs -> 68.5μs (16.4% faster)
    # Should return '0 1 2 ... 999'
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_exact_cap():
    # Input exactly 1000
    codeflash_output = funcA(1000); result = codeflash_output # 77.9μs -> 66.4μs (17.2% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_just_below_cap():
    # Input just below the cap
    codeflash_output = funcA(999); result = codeflash_output # 77.3μs -> 66.1μs (17.0% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_just_above_cap():
    # Input just above the cap
    codeflash_output = funcA(1001); result = codeflash_output # 77.3μs -> 66.0μs (17.1% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_float_input():
    # Non-integer input: should raise TypeError
    with pytest.raises(TypeError):
        funcA(5.5)

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

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


# 3. Large Scale Test Cases

def test_funcA_large_scale_500():
    # Test with a large number (500)
    codeflash_output = funcA(500); result = codeflash_output # 41.0μs -> 34.7μs (18.0% faster)
    expected = " ".join(str(i) for i in range(500))

def test_funcA_large_scale_999():
    # Test with the largest number below cap (999)
    codeflash_output = funcA(999); result = codeflash_output # 78.2μs -> 66.7μs (17.2% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_large_scale_1000():
    # Test with the maximum allowed number (1000)
    codeflash_output = funcA(1000); result = codeflash_output # 77.5μs -> 66.1μs (17.3% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_large_scale_above_1000():
    # Test with a number above the cap (e.g., 1234)
    codeflash_output = funcA(1234); result = codeflash_output # 77.4μs -> 66.4μs (16.6% faster)
    expected = " ".join(str(i) for i in range(1000))

# Additional edge: test with input 2 (smallest multi-digit)
def test_funcA_two():
    codeflash_output = funcA(2) # 2.88μs -> 2.17μs (32.7% faster)

# Additional edge: test with input -1 (negative just below zero)
def test_funcA_negative_one():
    codeflash_output = funcA(-1) # 2.29μs -> 1.38μs (66.0% faster)

# Additional edge: test with input 1000 (max allowed)
def test_funcA_max_allowed():
    codeflash_output = funcA(1000); result = codeflash_output # 78.2μs -> 66.8μs (17.0% faster)

# Additional: test with input as boolean (should treat as int)
def test_funcA_bool_true():
    # True is 1, so should return '0'
    codeflash_output = funcA(True) # 2.90μs -> 2.07μs (39.6% faster)

def test_funcA_bool_false():
    # False is 0, so should return ''
    codeflash_output = funcA(False) # 2.38μs -> 1.56μs (52.6% faster)

# Additional: test with input as a very large negative number
def test_funcA_very_large_negative():
    codeflash_output = funcA(-10000) # 2.43μs -> 1.44μs (68.8% faster)

# Additional: test with input as a large positive number at the cap
def test_funcA_very_large_positive():
    # Should be capped at 1000
    codeflash_output = funcA(10000); result = codeflash_output # 77.5μs -> 66.7μs (16.2% faster)
    expected = " ".join(str(i) for i in range(1000))
# 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 0: should return an empty string
    codeflash_output = funcA(0) # 2.29μs -> 1.53μs (49.1% faster)

def test_funcA_one():
    # Test with 1: should return "0"
    codeflash_output = funcA(1) # 2.67μs -> 2.00μs (33.0% faster)

def test_funcA_small_number():
    # Test with a small number (5)
    codeflash_output = funcA(5) # 3.06μs -> 2.38μs (28.2% faster)

def test_funcA_typical_number():
    # Test with a typical value (10)
    codeflash_output = funcA(10) # 3.38μs -> 2.77μs (21.7% faster)

# === Edge Test Cases ===

def test_funcA_negative_number():
    # Test with a negative number: should return an empty string, as range(-5) is empty
    codeflash_output = funcA(-5) # 2.26μs -> 1.41μs (60.2% faster)

def test_funcA_large_number_limit():
    # Test with number exactly at the cap (1000)
    codeflash_output = funcA(1000); result = codeflash_output # 78.2μs -> 66.7μs (17.3% faster)
    # Should have exactly 1000 numbers
    numbers = result.split()

def test_funcA_above_limit():
    # Test with number above the cap (e.g., 1234)
    codeflash_output = funcA(1234); result = codeflash_output # 77.6μs -> 65.7μs (18.2% faster)
    # Should behave as if number is 1000
    numbers = result.split()

def test_funcA_limit_plus_one():
    # Test with number just above the cap (1001)
    codeflash_output = funcA(1001); result = codeflash_output # 77.4μs -> 66.0μs (17.2% faster)
    numbers = result.split()

def test_funcA_limit_minus_one():
    # Test with number just below the cap (999)
    codeflash_output = funcA(999); result = codeflash_output # 77.5μs -> 66.1μs (17.1% faster)
    numbers = result.split()

def test_funcA_non_integer_float():
    # 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)

# === Large Scale Test Cases ===

def test_funcA_large_scale_500():
    # Test with a large but manageable number (500)
    codeflash_output = funcA(500); result = codeflash_output # 40.7μs -> 34.5μs (18.3% faster)
    numbers = result.split()

def test_funcA_large_scale_999():
    # Test with the largest number below the cap (999)
    codeflash_output = funcA(999); result = codeflash_output # 78.9μs -> 72.3μs (9.10% faster)
    numbers = result.split()

def test_funcA_large_scale_1000():
    # Test with the cap value (1000)
    codeflash_output = funcA(1000); result = codeflash_output # 77.1μs -> 66.5μs (16.1% faster)
    numbers = result.split()

def test_funcA_large_scale_upper_bound():
    # Test with a value much larger than the cap (e.g., 9999)
    codeflash_output = funcA(9999); result = codeflash_output # 77.8μs -> 66.0μs (17.8% faster)
    numbers = result.split()

# === Additional Edge Cases ===

def test_funcA_input_is_bool():
    # Test with boolean input (True/False)
    # True is 1, so should return "0"
    codeflash_output = funcA(True) # 2.90μs -> 2.05μs (41.0% faster)
    # False is 0, so should return ""
    codeflash_output = funcA(False) # 1.35μs -> 912ns (48.2% faster)

def test_funcA_input_is_large_negative():
    # Test with a large negative number
    codeflash_output = funcA(-999) # 2.50μs -> 1.45μs (71.8% 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([5])

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

# === Determinism Test ===

def test_funcA_determinism():
    # Multiple calls with the same input should yield the same result
    codeflash_output = funcA(15); result1 = codeflash_output # 4.06μs -> 3.29μs (23.5% faster)
    codeflash_output = funcA(15); result2 = codeflash_output # 2.35μs -> 2.02μs (16.4% faster)

# === Mutation Testing Guards ===

def test_funcA_no_extra_spaces():
    # Make sure there are no leading/trailing spaces
    codeflash_output = funcA(10); result = codeflash_output # 3.32μs -> 2.62μs (26.3% faster)

def test_funcA_all_numbers_are_correct():
    # Check that all numbers are present and in order for a random n
    n = 123
    codeflash_output = funcA(n); result = codeflash_output # 12.1μs -> 10.3μs (18.4% faster)
    numbers = [int(x) for x in result.split()]
# 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-mccvocho and push.