⚡️ Speed up function funcA by 9%

[codeflash-ai]

📄 9% (0.09x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 1.53 milliseconds → 1.40 milliseconds (best of 339 runs)

📝 Explanation and details

Here's an optimized rewrite of your function.
Analysis:
Your bottleneck (95.5% of time) is in " ".join(map(str, range(number))) — specifically, the str conversion for every integer when number is large.
Optimization:

• Preallocate a list of the required size and write to it directly (avoids the moderately expensive repeated calls to str()).
• Use a generator expression instead of map isn’t measurably faster here, but a list comprehension allows us to preallocate and assign in-place via list assignment.
• For this problem, using str.join is already efficient, but there's a classic faster trick:
  • Write numbers as bytes, then decode. However, in Python 3, for typical numbers the gain is marginal over " ".join(...).
• However, a measurable improvement is possible by.
  • Using a cached local variable for str (micro-optimization).
  • Using f-strings in Python 3.6+ doesn't benefit here.
• Best possible standard optimization: Use a list comprehension with local variable aliasing.

Fastest approach in idiomatic Python.

Notes:

• Local variable lookup (to_str) is faster than global lookup (str) in tight loops.
• In some environments, using array.array or numpy arrays can offer speedup, but for string conversion, the above is most reliable.

Ultra-fast method: Write all digits, minimal Python allocation (micro-optimized)

• For number <=1000, the memory cost is fine.
• But: On CPython, " ".join([str(i) for i in range(number)]) is already very well optimized and the above is only slightly faster for large N.

Final recommended, clean and still faster version.

Summary:

• Your code was already quite optimal in terms of Pythonic speed. The micro-optimization of binding str locally and using list comprehension gives a small but measurable speedup.
• For trivial values of number up to 1000, further optimization would require changing the language/runtime (e.g., C extension).

If absolute minimum runtime is needed:
Consider using Cython, Numba, or a C extension for this particular tight loop. For pure Python, the above is as fast as it gets.

---

Let me know if you want Numba/Cython versions or if your use-case involves N≫1000.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 62 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 number = 0 (should return an empty string)
    codeflash_output = funcA(0) # 1.81μs -> 731ns (148% faster)

def test_funcA_one():
    # Test with number = 1 (should return '0')
    codeflash_output = funcA(1) # 2.17μs -> 2.21μs (1.41% slower)

def test_funcA_small_number():
    # Test with a small number (should return space-separated numbers from 0 to n-1)
    codeflash_output = funcA(5) # 2.44μs -> 2.71μs (9.98% slower)
    codeflash_output = funcA(3) # 1.07μs -> 1.10μs (2.72% slower)

def test_funcA_typical_number():
    # Test with a typical number within range
    codeflash_output = funcA(10) # 2.87μs -> 2.98μs (4.02% slower)

# 2. Edge Test Cases

def test_funcA_negative_number():
    # Test with a negative number (should return empty string, as range(negative) is empty)
    codeflash_output = funcA(-1) # 1.71μs -> 1.72μs (0.522% slower)
    codeflash_output = funcA(-100) # 781ns -> 691ns (13.0% faster)

def test_funcA_large_number_limit():
    # Test with number exactly at the cap (1000)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1000) # 76.2μs -> 69.0μs (10.3% faster)

def test_funcA_above_large_number_limit():
    # Test with number above the cap (should still cap at 1000)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1500) # 75.7μs -> 68.6μs (10.2% faster)
    codeflash_output = funcA(1001) # 74.4μs -> 67.4μs (10.4% faster)

def test_funcA_non_integer_input():
    # Test with non-integer input (float) - should raise TypeError
    with pytest.raises(TypeError):
        funcA(3.5)
    with pytest.raises(TypeError):
        funcA("10")
    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) # 2.36μs -> 2.45μs (3.67% slower)
    codeflash_output = funcA(False) # 1.07μs -> 491ns (118% faster)

def test_funcA_minimum_integer():
    # Test with minimum possible integer (simulate very negative)
    codeflash_output = funcA(-2**31) # 1.67μs -> 1.82μs (8.23% slower)

def test_funcA_maximum_integer():
    # Test with maximum possible integer (simulate very large, should cap at 1000)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(2**31 - 1) # 75.7μs -> 68.7μs (10.3% faster)

# 3. Large Scale Test Cases

def test_funcA_large_scale_typical():
    # Test with number = 999 (just below cap)
    expected = " ".join(str(i) for i in range(999))
    codeflash_output = funcA(999) # 75.4μs -> 68.1μs (10.6% faster)

def test_funcA_large_scale_at_cap():
    # Test with number = 1000 (at cap)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1000) # 75.5μs -> 68.3μs (10.6% faster)

def test_funcA_large_scale_above_cap():
    # Test with number = 10000 (well above cap)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(10000) # 75.6μs -> 68.5μs (10.4% faster)

def test_funcA_performance_large_input():
    # Test performance for large input (should not be slow or memory-intensive)
    # This is a soft test: just ensure it completes and output is correct
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(99999) # 75.5μs -> 67.9μs (11.2% faster)

# Additional edge cases for robustness

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 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_none():
    # Test with 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 number = 0, should return an empty string
    codeflash_output = funcA(0) # 1.91μs -> 891ns (115% faster)

def test_funcA_one():
    # Test with number = 1, should return "0"
    codeflash_output = funcA(1) # 2.24μs -> 2.28μs (1.75% slower)

def test_funcA_small_number():
    # Test with a small number
    codeflash_output = funcA(5) # 2.58μs -> 2.77μs (6.85% slower)

def test_funcA_typical_number():
    # Test with a typical number
    codeflash_output = funcA(10) # 2.88μs -> 3.02μs (4.63% slower)

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

def test_funcA_negative_number():
    # Negative number should produce empty string (range(negative) is empty)
    codeflash_output = funcA(-5) # 1.72μs -> 1.69μs (1.83% faster)

def test_funcA_float_input():
    # Floats should raise TypeError, as range expects integers
    with pytest.raises(TypeError):
        funcA(5.5)

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

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

def test_funcA_large_input_capped():
    # Input above 1000 should be capped at 1000
    codeflash_output = funcA(1500); result = codeflash_output # 86.9μs -> 82.8μs (5.05% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_input_at_cap():
    # Input exactly at cap should return numbers 0 to 999
    codeflash_output = funcA(1000); result = codeflash_output # 76.9μs -> 70.1μs (9.72% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_input_just_below_cap():
    # Input just below cap should return numbers 0 to 998
    codeflash_output = funcA(999); result = codeflash_output # 76.7μs -> 69.4μs (10.6% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_input_just_above_cap():
    # Input just above cap should still return numbers 0 to 999
    codeflash_output = funcA(1001); result = codeflash_output # 77.0μs -> 69.8μs (10.3% faster)
    expected = " ".join(str(i) for i in range(1000))

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

def test_funcA_input_is_bool():
    # Input is boolean True (should be treated as 1)
    codeflash_output = funcA(True) # 2.33μs -> 2.60μs (10.0% slower)
    # Input is boolean False (should be treated as 0)
    codeflash_output = funcA(False) # 1.02μs -> 501ns (104% faster)

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

def test_funcA_large_scale_500():
    # Test with 500, should return string of numbers 0..499
    codeflash_output = funcA(500); result = codeflash_output # 39.5μs -> 36.6μs (7.85% faster)
    expected = " ".join(str(i) for i in range(500))

def test_funcA_large_scale_999():
    # Test with 999, should return string of numbers 0..998
    codeflash_output = funcA(999); result = codeflash_output # 77.5μs -> 70.4μs (10.1% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_large_scale_max():
    # Test with 1000, should return string of numbers 0..999
    codeflash_output = funcA(1000); result = codeflash_output # 77.0μs -> 70.2μs (9.69% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_large_scale_above_max():
    # Test with 10000, should be capped at 1000
    codeflash_output = funcA(10000); result = codeflash_output # 77.3μs -> 70.1μs (10.3% faster)
    expected = " ".join(str(i) for i in range(1000))

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

def test_funcA_input_is_list():
    # Input is a list, should raise TypeError
    with pytest.raises(TypeError):
        funcA([10])

def test_funcA_input_is_dict():
    # Input is a dict, should raise TypeError
    with pytest.raises(TypeError):
        funcA({'number': 10})

def test_funcA_input_is_tuple():
    # Input is a tuple, should raise TypeError
    with pytest.raises(TypeError):
        funcA((10,))

def test_funcA_input_is_complex():
    # Input is a complex number, should raise TypeError
    with pytest.raises(TypeError):
        funcA(5+3j)

def test_funcA_input_is_large_negative():
    # Test with a large negative number, should return empty string
    codeflash_output = funcA(-1000) # 1.87μs -> 1.89μs (1.06% slower)

def test_funcA_input_is_minimum_integer():
    # Test with minimum integer, should return empty string
    import sys
    codeflash_output = funcA(-sys.maxsize) # 1.69μs -> 1.77μs (4.51% slower)

# -------------------------------
# Determinism Test
# -------------------------------

def test_funcA_determinism():
    # Calling funcA multiple times with same input should yield same result
    for n in [0, 1, 10, 100, 500, 1000]:
        codeflash_output = funcA(n); result1 = codeflash_output
        codeflash_output = funcA(n); result2 = codeflash_output
# 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-mcl4doly and push.