⚡️ 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 : 254 microseconds → 239 microseconds (best of 719 runs)

📝 Explanation and details

Here's an optimized version of your code that minimizes unnecessary computation and improves string concatenation performance for speed, especially for larger values of number.
Specifically.

• Uses a single call to ' '.join() with a generator expression, but since you already did this, it is already optimal for memory.
• Avoids re-computing min(1000, number) in funcA and ensures efficient cache lookup.
• Uses a tuple comprehension in the cache key to improve memory (although unnecessary for a single argument, not changed for function signature parity).
• Uses built-in str.join but leverages the fact that ' '.join(map(str, ...)) is already pretty optimal, but we can use list comprehension for slight speed boost in some Python versions.

However, the key bottleneck in your code is string joining for large N; thus, the real further boost is to use a precomputed string table for all numbers up to 1000 for instant lookup, at the expense of a tiny bit of initialization time and memory — since your number argument is capped.

Here's such a re-write.

Why this is faster

• All possible output strings (for number in [0, 1000]) are pre-built once upfront; every call is an O(1) lookup, which is far faster than building a new string or hitting the LRU cache repeatedly.
• No change to the output semantics or API.
• LRU cache is replaced with a lookup table, which is strictly faster in this use case and bounded in size.

If you require the @lru_cache for stylistic/compatibility reasons, you could keep it; but performance will be a bit slower than the static lookup above.

---

If you want a strictly minimal change, the following is also marginally faster than your code for small N:

But the precomputed table version at the top is the fastest possible for your constraints.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 59 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 empty string
    codeflash_output = funcA(0) # 2.81μs -> 2.38μs (18.1% faster)

def test_funcA_one():
    # Test with input 1, should return "0"
    codeflash_output = funcA(1) # 2.88μs -> 2.58μs (11.2% faster)

def test_funcA_small_number():
    # Test with small input, should return numbers 0 to n-1 as space-separated string
    codeflash_output = funcA(5) # 3.40μs -> 3.33μs (2.10% faster)

def test_funcA_typical_number():
    # Test with a typical number, e.g., 10
    codeflash_output = funcA(10) # 992ns -> 971ns (2.16% faster)

def test_funcA_number_as_string_equivalence():
    # Ensure output is space-separated string, not list or other type
    codeflash_output = funcA(3); result = codeflash_output # 3.13μs -> 2.96μs (6.09% faster)


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

def test_funcA_negative_number():
    # Negative input should yield empty string (range(negative) is empty)
    codeflash_output = funcA(-5) # 2.43μs -> 2.16μs (12.5% faster)

def test_funcA_large_number_capped_at_1000():
    # Input > 1000 should be capped at 1000, so output is "0 1 ... 999"
    codeflash_output = funcA(2000); result = codeflash_output # 78.7μs -> 72.6μs (8.37% faster)
    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); result = codeflash_output # 1.06μs -> 1.13μs (6.18% slower)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_just_below_1000():
    # Input 999 should return numbers 0 to 998
    codeflash_output = funcA(999); result = codeflash_output # 77.2μs -> 71.3μs (8.23% faster)
    expected = " ".join(str(i) for i in range(999))

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

def test_funcA_non_numeric_input():
    # Non-numeric input should raise TypeError
    with pytest.raises(TypeError):
        funcA("100")

def test_funcA_bool_input():
    # Boolean input (True==1, False==0) should behave as int
    codeflash_output = funcA(True) # 3.63μs -> 3.42μs (6.15% faster)
    codeflash_output = funcA(False) # 1.43μs -> 1.33μs (7.51% faster)

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

def test_funcA_extreme_negative():
    # Very large negative input should return empty string
    codeflash_output = funcA(-1000000) # 3.01μs -> 2.65μs (13.3% faster)


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

def test_funcA_performance_large_input():
    # Test with the largest allowed input (1000)
    codeflash_output = funcA(1000); result = codeflash_output # 1.13μs -> 1.15μs (1.74% slower)
    # Check length: "0" to "999" separated by spaces
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_performance_near_limit():
    # Test with input just below the cap
    codeflash_output = funcA(999); result = codeflash_output # 1.13μs -> 1.14μs (0.876% slower)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_multiple_calls_cache():
    # Test repeated calls to check caching doesn't affect correctness
    for n in [0, 1, 10, 100, 999, 1000]:
        expected = " ".join(str(i) for i in range(min(1000, n)))
        for _ in range(3):
            codeflash_output = funcA(n)

def test_funcA_all_integers_up_to_20():
    # Test all integers from 0 to 20 for correctness
    for n in range(21):
        expected = " ".join(str(i) for i in range(n))
        codeflash_output = funcA(n)

def test_funcA_input_at_cache_boundary():
    # Test input at cache boundary (1001, should be capped to 1000)
    codeflash_output = funcA(1001); result = codeflash_output # 1.10μs -> 1.15μs (4.26% slower)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_large_negative_and_large_positive():
    # Test both large negative and large positive inputs
    codeflash_output = funcA(-999999) # 2.89μs -> 2.50μs (15.2% faster)
    codeflash_output = funcA(999999) # 651ns -> 631ns (3.17% faster)
# 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-mccv6d28 and push.