⚡️ Speed up function funcA by 12%

[codeflash-ai]

📄 12% (0.12x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 246 microseconds → 219 microseconds (best of 444 runs)

📝 Explanation and details

Certainly! Here are the main efficiency aspects for this code.

• The only real computation is converting a sequence of numbers (from 0 to n-1) into a space-separated string.
• lru_cache is already helping for repeated calls with the same argument, so further internal caching won't help much.
• However, str.join(map(str, range(n))) can still be optimized for Python (especially for big n), since " ".join(map(str, ...)) builds many intermediate strings.

Optimization Approach

The main optimization possible.

• Use a direct string concatenation method that's faster for large inputs, avoiding generator overhead.
• Pre-allocate and fill a list of all number strings, then join once.

However, " ".join(...list of strings...) is already quite fast and optimal in CPython, so the improvement is limited and mostly negligible unless used at very large n.

But we can do a bit better with.

• Use a faster conversion for small n (shortcut for n == 0, n == 1).
• Avoid map completely; list comprehension is just as fast or faster in Py3.

Additional Micro-optimization

• Avoid min(number, 1000) by using an early return if input number >= 1000 (helps with call overhead if called with huge numbers frequently).

Here's the rewritten code with faster runtime.

Changes.

• Early exits for n == 0 and n == 1 (eliminates unnecessary work for tiny inputs).
• Slightly faster list comprehension for string conversion.
• Reduced overhead in the conditional min.
• Preserved all original cache semantics and function signatures.
• No unnecessary map generator creation.

Result.

• Slightly lower runtime overhead, especially for small n and at scale.
• Lower memory overhead by not keeping an intermediate generator alive.
• The code remains simple and clean.

If you use funcA repeatedly with numbers 0–1000, this is about as fast as Python can go for this problem!

Let me know if you have a larger scale or further constraints.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 31 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

# 1. Basic Test Cases

def test_funcA_zero():
    # Test with 0: should return empty string as range(0) is empty
    codeflash_output = funcA(0) # 2.40μs -> 360ns (565% faster)

def test_funcA_one():
    # Test with 1: should return "0"
    codeflash_output = funcA(1) # 2.67μs -> 912ns (193% faster)

def test_funcA_small_number():
    # Test with a small number
    codeflash_output = funcA(5) # 3.15μs -> 2.98μs (5.75% faster)

def test_funcA_typical_number():
    # Test with a typical number
    codeflash_output = funcA(10) # 781ns -> 521ns (49.9% faster)

def test_funcA_number_as_exact_limit():
    # Test with number exactly at the limit (1000)
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1000) # 78.0μs -> 72.3μs (7.80% faster)

# 2. Edge Test Cases

def test_funcA_negative_number():
    # Negative input should behave like range(0): empty string
    codeflash_output = funcA(-5) # 2.13μs -> 300ns (611% faster)

def test_funcA_large_number_above_limit():
    # Input above 1000 should be capped at 1000
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1500) # 912ns -> 702ns (29.9% faster)

def test_funcA_limit_plus_one():
    # Input just above limit
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1001) # 891ns -> 661ns (34.8% faster)

def test_funcA_limit_minus_one():
    # Input just below limit
    expected = " ".join(str(i) for i in range(999))
    codeflash_output = funcA(999) # 77.3μs -> 71.5μs (8.11% faster)

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

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

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

def test_funcA_bool_input():
    # Boolean input: True is 1, False is 0
    codeflash_output = funcA(True) # 3.00μs -> 1.20μs (149% faster)
    codeflash_output = funcA(False) # 1.62μs -> 281ns (478% faster)

def test_funcA_mutation_sensitivity():
    # If function skips 0, or changes separator, or off-by-one, this will fail
    codeflash_output = funcA(3) # 2.77μs -> 2.71μs (2.26% faster)
    # Check for correct separator (not comma or other)
    codeflash_output = funcA(10) # 461ns -> 351ns (31.3% faster)
    codeflash_output = funcA(10) # 351ns -> 221ns (58.8% faster)

# 3. Large Scale Test Cases

def test_funcA_large_scale_500():
    # Test with a large but not maximal value
    expected = " ".join(str(i) for i in range(500))
    codeflash_output = funcA(500) # 39.8μs -> 36.9μs (7.97% faster)

def test_funcA_large_scale_999():
    # Test with just below the limit
    expected = " ".join(str(i) for i in range(999))
    codeflash_output = funcA(999) # 881ns -> 561ns (57.0% faster)

def test_funcA_large_scale_1000():
    # Test with the upper limit
    expected = " ".join(str(i) for i in range(1000))
    codeflash_output = funcA(1000) # 841ns -> 571ns (47.3% faster)

def test_funcA_large_scale_performance():
    # Test that calling funcA repeatedly with large values is performant (due to caching)
    import time
    start = time.time()
    for _ in range(10):
        codeflash_output = funcA(1000); result = codeflash_output
    elapsed = time.time() - start


def test_funcA_idempotence():
    # Multiple calls with same input yield same result
    codeflash_output = funcA(7) # 3.66μs -> 3.65μs (0.274% faster)

To edit these changes git checkout codeflash/optimize-funcA-mccv3vlv and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #397 by codeflash-ai[bot] was closed.