⚡️ Speed up function funcA by 6%

[codeflash-ai]

📄 6% (0.06x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 76.6 milliseconds → 72.3 milliseconds (best of 58 runs)

📝 Explanation and details

Here is an optimized version of your program.
Key improvements.

• Remove unnecessary comment and assignment for j (since you said the value/variable should be retained, I keep its assignment but comment on it).
• Limit object creation by using a tuple as the cache key (already done, since lru_cache sees the number parameter as hashable).
• map(str, range(number)) is already fast; however, for even better runtime, join over a list comprehension (list comprehension is generally slightly faster than map(str, ...) in Python ≥3.7 due to interpreter optimizations) and remove the min from the cache by doing it outside (as soon as possible in funcA).
• Avoid repeated computation of min(1000, number) in the cache decorator.

Why this is faster:

• The use of list comprehension is usually a bit faster with primitive types.
• The unnecessary computation of min() is done outside of the lru_cache, reducing redundant cache keys and lookups.
• Kept your unused assignment as per your requirements.

If you want maximum throughput and the number argument is always a non-negative integer, this is about as fast as you can get using pure Python and lru_cache. (For huge-scale performance, a C-extension or writing directly to a buffer would be the next step, but is unnecessary here.)

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 2161 Passed
⏪ Replay Tests	✅ 3 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from functools import lru_cache

# imports
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) # 1.40μs -> 1.39μs (0.646% faster)

def test_funcA_one():
    # Test with input 1 (should return "0")
    codeflash_output = funcA(1) # 1.21μs -> 1.23μs (1.70% slower)

def test_funcA_small_number():
    # Test with a small number, e.g., 5
    codeflash_output = funcA(5) # 1.15μs -> 1.15μs (0.000% faster)

def test_funcA_typical_number():
    # Test with a typical number, e.g., 10
    codeflash_output = funcA(10) # 1.19μs -> 1.18μs (0.931% faster)

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

def test_funcA_negative_number():
    # Negative numbers should result in an empty string (range(negative) is empty)
    codeflash_output = funcA(-1) # 3.32μs -> 3.13μs (6.08% faster)
    codeflash_output = funcA(-100) # 1.48μs -> 1.41μs (4.95% faster)

def test_funcA_large_number_exact_limit():
    # Input exactly 1000 should produce "0 1 2 ... 999"
    codeflash_output = funcA(1000); result = codeflash_output # 82.7μs -> 77.7μs (6.47% faster)
    parts = result.split()

def test_funcA_large_number_above_limit():
    # Input above 1000 should be capped at 1000
    codeflash_output = funcA(1500); result = codeflash_output # 1.25μs -> 1.22μs (2.54% faster)
    parts = result.split()

def test_funcA_non_integer_input():
    # Non-integer input should raise TypeError
    with pytest.raises(TypeError):
        funcA("10")
    with pytest.raises(TypeError):
        funcA(10.5)
    with pytest.raises(TypeError):
        funcA(None)

def test_funcA_boolean_input():
    # Booleans are subclasses of int in Python, so True==1, False==0
    codeflash_output = funcA(True) # 3.90μs -> 3.86μs (1.04% faster)
    codeflash_output = funcA(False) # 1.75μs -> 1.57μs (11.5% faster)

def test_funcA_minimum_integer():
    # Test with the minimum possible integer (simulate very large negative)
    codeflash_output = funcA(-2**63) # 3.41μs -> 3.24μs (5.25% faster)

def test_funcA_maximum_integer():
    # Test with a very large integer (simulate very large positive)
    codeflash_output = funcA(10**18); result = codeflash_output # 1.25μs -> 1.27μs (1.57% slower)
    parts = result.split()

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

def test_funcA_performance_large():
    # Test performance and correctness with n=999 (just below cap)
    codeflash_output = funcA(999); result = codeflash_output # 1.37μs -> 1.32μs (3.70% faster)
    parts = result.split()

def test_funcA_performance_cap():
    # Test performance and correctness with n=1000 (at cap)
    codeflash_output = funcA(1000); result = codeflash_output # 1.21μs -> 1.26μs (3.88% slower)
    parts = result.split()

def test_funcA_performance_above_cap():
    # Test performance and correctness with n=1001 (above cap)
    codeflash_output = funcA(1001); result = codeflash_output # 1.18μs -> 1.21μs (2.56% slower)
    parts = result.split()

def test_funcA_all_unique_outputs_under_cap():
    # Ensure that for every n in 0..1000, funcA(n) is correct and unique
    seen = set()
    for n in range(0, 1000):
        codeflash_output = funcA(n); s = codeflash_output
        seen.add(s)
        # Should have n items (unless n==0)
        if n == 0:
            pass
        else:
            parts = s.split()

def test_funcA_cache_efficiency():
    # Call funcA repeatedly with the same value and ensure result is same and fast
    import time
    n = 500
    codeflash_output = funcA(n); result1 = codeflash_output # 1.75μs -> 1.61μs (8.68% faster)
    start = time.time()
    for _ in range(100):
        codeflash_output = funcA(n)
    end = time.time()

# -------------------------------
# Additional Robustness Cases
# -------------------------------

def test_funcA_mutation_resistance():
    # Changing the join separator or the range should break the tests above
    # This is a meta-test: if funcA is mutated, most tests should fail
    # Here we just ensure the output is exactly as expected for a few cases
    codeflash_output = funcA(3) # 1.19μs -> 1.21μs (1.57% slower)
    codeflash_output = funcA(7) # 561ns -> 581ns (3.44% slower)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from functools import lru_cache

# imports
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 empty string
    codeflash_output = funcA(0) # 2.77μs -> 2.50μs (11.2% faster)

def test_funcA_one():
    # Test with 1: Should return "0"
    codeflash_output = funcA(1) # 2.94μs -> 2.79μs (5.75% faster)

def test_funcA_small_number():
    # Test with a small number (5): Should return "0 1 2 3 4"
    codeflash_output = funcA(5) # 3.60μs -> 3.55μs (1.41% faster)

def test_funcA_typical_number():
    # Test with a typical number (10): Should return "0 1 2 3 4 5 6 7 8 9"
    codeflash_output = funcA(10) # 1.03μs -> 1.03μs (0.097% faster)

def test_funcA_number_as_string():
    # Test with string input should raise TypeError
    with pytest.raises(TypeError):
        funcA("5")

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

# --- Edge Test Cases ---

def test_funcA_negative_number():
    # Negative number: Should return empty string (range(negative) is empty)
    codeflash_output = funcA(-10) # 2.81μs -> 2.44μs (15.2% faster)

def test_funcA_large_number_exact_limit():
    # Test with 1000: Should return numbers 0 to 999
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1000) # 79.8μs -> 74.8μs (6.64% faster)

def test_funcA_above_limit():
    # Test with number above 1000: Should cap at 1000
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1500) # 1.21μs -> 1.26μs (3.96% slower)

def test_funcA_limit_plus_one():
    # Test with 1001: Should cap at 1000
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1001) # 1.22μs -> 1.28μs (4.68% slower)

def test_funcA_minimum_integer():
    # Test with minimum integer (simulate a very negative number)
    codeflash_output = funcA(-999999) # 3.25μs -> 2.94μs (10.6% faster)

def test_funcA_large_negative():
    # Test with -1: Should return empty string
    codeflash_output = funcA(-1) # 2.67μs -> 2.45μs (8.60% faster)

def test_funcA_boolean_input():
    # Test with boolean input: True == 1, False == 0
    codeflash_output = funcA(True) # 3.71μs -> 3.54μs (4.81% faster)
    codeflash_output = funcA(False) # 1.48μs -> 1.35μs (9.69% faster)

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_999():
    # Large but under cap: 999
    expected = " ".join(str(i) for i in range(999))
    codeflash_output = funcA(999) # 78.3μs -> 72.1μs (8.67% faster)

def test_funcA_large_scale_1000():
    # At cap: 1000
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1000) # 1.26μs -> 1.28μs (1.56% slower)

def test_funcA_large_scale_just_above_cap():
    # Above cap: 1005 should be capped at 1000
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1005) # 1.19μs -> 1.27μs (6.36% slower)

def test_funcA_performance_cache():
    # Test repeated calls (cache effectiveness, correctness)
    codeflash_output = funcA(1000); result1 = codeflash_output # 1.03μs -> 1.15μs (10.4% slower)
    codeflash_output = funcA(1000); result2 = codeflash_output # 531ns -> 530ns (0.189% faster)
    # Changing argument returns different result
    codeflash_output = funcA(999) # 470ns -> 491ns (4.28% slower)

def test_funcA_all_digits():
    # Test that the output contains all numbers in correct order for a mid-large input
    n = 123
    codeflash_output = funcA(n); output = codeflash_output # 13.2μs -> 12.8μs (3.37% faster)
    expected = " ".join(str(i) for i in range(n))

def test_funcA_no_trailing_space():
    # Output should not have trailing whitespace
    for n in [1, 5, 100, 1000]:
        codeflash_output = funcA(n); result = codeflash_output

def test_funcA_idempotence():
    # Calling funcA multiple times with same argument should always return same result
    for n in [0, 1, 10, 999, 1000]:
        codeflash_output = funcA(n); r1 = codeflash_output
        codeflash_output = funcA(n); r2 = codeflash_output

# --- Additional Edge Cases ---

def test_funcA_large_gap():
    # Test with a very large positive number (simulate potential overflow)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(10**9) # 1.16μs -> 1.20μs (3.41% slower)

def test_funcA_maxsize_cache():
    # Test cache limit: fill cache with unique values and ensure correctness
    for n in range(1000):
        expected = " ".join(str(i) for i in range(n))
        codeflash_output = funcA(n)

def test_funcA_non_integer_input():
    # Test with non-integer types: list, dict, etc.
    with pytest.raises(TypeError):
        funcA([1,2,3])
    with pytest.raises(TypeError):
        funcA({'a': 1})
    with pytest.raises(TypeError):
        funcA((5,))
# 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-mccuuwnv and push.