⚡️ Speed up function funcA by 473%

[codeflash-ai]

📄 473% (4.73x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 1.91 milliseconds → 333 microseconds (best of 345 runs)

📝 Explanation and details

Here’s a faster version of your program, trading code compactness for runtime efficiency by using a custom list comprehension and avoiding multiple layers of iteration.

Rationale for Optimizations

• The bottleneck is " ".join(map(str, range(number))), which separately creates an iterator and converts each number to string on the fly.
• A list comprehension is faster here than map(str, ...) for small numbers (<1000) because Python can allocate and fill an array of known size more efficiently.
• Explicitly returns an empty string when number <= 0 to avoid unnecessary work and handle edge cases.
• Avoided generator expressions/generators for " ".join() because list comprehensions are faster for small, fixed sizes.

This is as fast as pure Python gets for this operation at these data sizes—further speedups would require use of external libraries or C extensions (such as NumPy for even larger ranges, which wouldn't help here since all outputs are strings).

Your code already does not compute any unused variables, so no further gains can be made there.

Note

If you are calling this function in a tight loop and need to squeeze out even more performance, consider turning the integer-to-string conversion into a lookup (for example, precomputing all string forms of 0–999 once and reusing them), but for number <= 1000 that's only a very minor improvement and typically not worth the extra code complexity.

If you want that micro-optimization.

This version eliminates all repeated integer-to-string conversions entirely for the target range.
"Best" version for repeated use in a hot loop!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 66 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) # 1.60μs -> 661ns (143% faster)

def test_funcA_one():
    # Test with input 1: should return "0"
    codeflash_output = funcA(1) # 2.11μs -> 1.02μs (107% faster)

def test_funcA_small_number():
    # Test with small input (5): should return "0 1 2 3 4"
    codeflash_output = funcA(5) # 2.38μs -> 1.10μs (116% faster)

def test_funcA_typical_number():
    # Test with a typical input (10): should return "0 1 2 3 4 5 6 7 8 9"
    codeflash_output = funcA(10) # 2.77μs -> 1.25μs (122% faster)

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

def test_funcA_negative_number():
    # Negative input: should return empty string (range(negative) is empty)
    codeflash_output = funcA(-5) # 1.66μs -> 661ns (152% faster)

def test_funcA_large_but_under_limit():
    # Input just below the cap (999): should return numbers 0 to 998
    codeflash_output = funcA(999); result = codeflash_output # 76.5μs -> 12.3μs (520% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_at_limit():
    # Input at the cap (1000): should return numbers 0 to 999
    codeflash_output = funcA(1000); result = codeflash_output # 77.0μs -> 12.3μs (527% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_above_limit():
    # Input above the cap (1001): should return numbers 0 to 999 (capped at 1000)
    codeflash_output = funcA(1001); result = codeflash_output # 76.7μs -> 12.2μs (531% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_far_above_limit():
    # Input far above the cap (5000): should still return numbers 0 to 999 (capped at 1000)
    codeflash_output = funcA(5000); result = codeflash_output # 76.6μs -> 12.3μs (522% faster)
    expected = " ".join(str(i) for i in range(1000))

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

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

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

def test_funcA_input_is_bool():
    # Boolean input: True is 1, False is 0
    codeflash_output = funcA(True) # 2.40μs -> 1.18μs (102% faster)
    codeflash_output = funcA(False) # 1.07μs -> 431ns (149% faster)

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

def test_funcA_large_input_performance():
    # Test with input at the maximum cap (1000)
    codeflash_output = funcA(1000); result = codeflash_output # 77.4μs -> 12.3μs (528% faster)

def test_funcA_large_input_below_cap():
    # Test with large input just below the cap (999)
    codeflash_output = funcA(999); result = codeflash_output # 76.4μs -> 12.2μs (525% faster)

def test_funcA_multiple_calls_consistency():
    # Call the function multiple times with large input to ensure no side effects
    for _ in range(10):
        codeflash_output = funcA(1000)

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

def test_funcA_input_is_large_negative():
    # Large negative input: should return empty string
    codeflash_output = funcA(-1000) # 1.77μs -> 651ns (172% faster)

def test_funcA_input_is_zero():
    # Input is zero: should return empty string
    codeflash_output = funcA(0) # 1.70μs -> 661ns (158% faster)

def test_funcA_input_is_maximum_integer():
    # Input is a very large integer (larger than cap)
    codeflash_output = funcA(10**6); result = codeflash_output # 77.5μs -> 12.1μs (539% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_input_is_minimum_integer():
    # Input is a very small integer (very negative)
    codeflash_output = funcA(-10**6) # 1.64μs -> 681ns (141% faster)

def test_funcA_input_is_object():
    # Input is an object: should raise TypeError
    class Dummy: pass
    with pytest.raises(TypeError):
        funcA(Dummy())

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

def test_funcA_input_is_tuple():
    # Input is a tuple: should raise TypeError
    with pytest.raises(TypeError):
        funcA((1,2,3))
# 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

# 1. Basic Test Cases

def test_funcA_zero():
    # Test with input 0 (should return empty string)
    codeflash_output = funcA(0) # 1.89μs -> 771ns (146% faster)

def test_funcA_one():
    # Test with input 1 (should return "0")
    codeflash_output = funcA(1) # 2.20μs -> 1.06μs (108% faster)

def test_funcA_small_number():
    # Test with small input (5)
    codeflash_output = funcA(5) # 2.46μs -> 1.21μs (103% faster)

def test_funcA_typical_number():
    # Test with a typical input (10)
    codeflash_output = funcA(10) # 2.81μs -> 1.22μs (130% faster)

# 2. Edge Test Cases

def test_funcA_negative_input():
    # Test with negative input (should return empty string, as range(negative) is empty)
    codeflash_output = funcA(-5) # 1.69μs -> 691ns (145% faster)

def test_funcA_large_input_exact_limit():
    # Test exactly at the limit (1000)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1000) # 76.2μs -> 12.1μs (527% faster)

def test_funcA_large_input_above_limit():
    # Test above the limit (e.g. 1500), should still cap at 1000
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1500) # 75.7μs -> 12.0μs (531% faster)

def test_funcA_input_is_float():
    # Test with float input (should raise TypeError, as range() expects int)
    with pytest.raises(TypeError):
        funcA(5.5)

def test_funcA_input_is_string():
    # Test with string input (should raise TypeError)
    with pytest.raises(TypeError):
        funcA("10")

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

def test_funcA_input_is_bool():
    # Test with boolean input (True acts as 1, False as 0)
    codeflash_output = funcA(True) # 2.42μs -> 1.17μs (107% faster)
    codeflash_output = funcA(False) # 1.00μs -> 460ns (118% faster)

def test_funcA_input_is_list():
    # Test with list input (should raise TypeError)
    with pytest.raises(TypeError):
        funcA([5])

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

def test_funcA_input_is_large_negative():
    # Test with a very large negative number
    codeflash_output = funcA(-1000) # 1.84μs -> 711ns (159% faster)

def test_funcA_input_is_min_integer():
    # Test with minimum integer (simulate, as Python ints are unbounded)
    codeflash_output = funcA(-2**63) # 1.72μs -> 771ns (123% faster)

def test_funcA_input_is_max_integer():
    # Test with a very large positive integer (should cap at 1000)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(2**63) # 76.6μs -> 12.2μs (529% faster)

# 3. Large Scale Test Cases

def test_funcA_large_scale_just_below_limit():
    # Test with 999 (just below cap)
    expected = " ".join(str(i) for i in range(999))
    codeflash_output = funcA(999) # 75.8μs -> 11.9μs (537% faster)

def test_funcA_large_scale_at_limit():
    # Test with 1000 (at cap)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1000) # 76.0μs -> 11.9μs (536% faster)

def test_funcA_large_scale_above_limit():
    # Test with 1001 (should cap at 1000)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1001) # 75.6μs -> 11.9μs (538% faster)

def test_funcA_performance_large_input(monkeypatch):
    # Test that function does not take too long for large input (simulate with time limit)
    import time
    start = time.time()
    codeflash_output = funcA(1000); result = codeflash_output # 76.7μs -> 12.3μs (525% faster)
    duration = time.time() - start
    # Also check correctness
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_output_formatting():
    # Test that output does not have trailing or leading spaces
    codeflash_output = funcA(10); result = codeflash_output # 2.93μs -> 1.33μs (120% faster)

def test_funcA_output_single_digit():
    # Test with input less than 10 (all single digit, no formatting issue)
    for n in range(10):
        codeflash_output = funcA(n); result = codeflash_output
        parts = result.split() if result else []

def test_funcA_output_multi_digit():
    # Test with input over 10 (should include multi-digit numbers)
    codeflash_output = funcA(15); result = codeflash_output # 3.18μs -> 1.25μs (154% faster)
    parts = 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-mcl39vwe and push.