⚡️ Speed up function funcA by 10%

[codeflash-ai]

📄 10% (0.10x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 1.16 milliseconds → 1.06 milliseconds (best of 387 runs)

📝 Explanation and details

Certainly! The biggest bottleneck in your code is the last line.

This is because string conversion and joining are relatively expensive, especially when repeated.

Optimization strategies

1. Avoid unnecessary calculations:
   k and j are calculated but unused. We can safely remove these lines for further optimization, unless you kept them for side-effect or debug purposes (your line profiling suggests they're dead code).

2. Faster int-to-str joining.

   • Precompute lookup for str(n) for small n.
   • Specialized approach for number <= 1000 (since you cap number at 1000).
   • Use a list comprehension and call " ".join() just once.
   • For even further optimization, use array module for preallocated data, but for under 1000 elements the gain is marginal.

Rewrite

Here is an optimized version using a tuple lookup for string conversions, which is faster than repeatedly calling str(n), and removes dead code.

Why this is faster

• Avoids str() lookup for each join element, since string conversion is done in a batch with list comprehension, which is faster than map(str, ...), especially for small numbers.
• Removes dead code computing k and j.
• For the actual join, join is as fast as it can be, but reducing per-element work (as above) helps.

---

(OPTIONAL) Precomputed cache version

If funcA(number) will be called many times with the same number values, you might precompute all such strings for number in [0, 1000].

This is very fast for repeated calls, at the cost of a few KB of memory.

---

Let me know if you want this extreme version, but for most purposes the list comprehension is the fastest idiomatic way in CPython for this operation.

---

Final optimized version:

This should run significantly faster for typical usage.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 45 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 input zero should return empty string
    codeflash_output = funcA(0) # 2.11μs -> 701ns (202% faster)

def test_funcA_one():
    # Test input one should return "0"
    codeflash_output = funcA(1) # 2.50μs -> 2.10μs (19.0% faster)

def test_funcA_small_positive():
    # Test small positive number
    codeflash_output = funcA(3) # 2.88μs -> 2.28μs (25.9% faster)
    codeflash_output = funcA(5) # 1.54μs -> 1.39μs (10.8% faster)

def test_funcA_typical():
    # Test a typical small number
    codeflash_output = funcA(10) # 3.29μs -> 2.93μs (12.3% faster)

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

def test_funcA_negative():
    # Negative input should return empty string
    codeflash_output = funcA(-5) # 2.19μs -> 1.58μs (38.6% faster)

def test_funcA_large_input_cap():
    # Input above 1000 should be capped at 1000
    codeflash_output = funcA(1500); result = codeflash_output # 79.1μs -> 72.9μs (8.50% faster)
    # Should be space-separated string from 0 to 999
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_input_exactly_1000():
    # Input exactly 1000 should return string from 0 to 999
    codeflash_output = funcA(1000); result = codeflash_output # 77.8μs -> 71.7μs (8.60% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_input_just_below_cap():
    # Input 999 should return string from 0 to 998
    codeflash_output = funcA(999); result = codeflash_output # 77.2μs -> 71.2μs (8.44% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_input_one_below_zero():
    # Input -1 should return empty string
    codeflash_output = funcA(-1) # 2.25μs -> 1.59μs (41.4% faster)

def test_funcA_input_non_integer():
    # Non-integer input: Should raise a TypeError
    with pytest.raises(TypeError):
        funcA("10")
    with pytest.raises(TypeError):
        funcA(5.5)
    with pytest.raises(TypeError):
        funcA(None)

def test_funcA_input_bool():
    # Boolean input: True is 1, False is 0
    codeflash_output = funcA(True) # 3.00μs -> 2.31μs (29.4% faster)
    codeflash_output = funcA(False) # 1.41μs -> 451ns (213% faster)

def test_funcA_input_large_negative():
    # Large negative input should return empty string
    codeflash_output = funcA(-1000000) # 2.52μs -> 1.62μs (55.5% faster)

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

def test_funcA_performance_large():
    # Test with a large allowed input (1000)
    codeflash_output = funcA(1000); result = codeflash_output # 78.1μs -> 72.2μs (8.18% faster)
    # Check first and last numbers
    split_result = result.split()

def test_funcA_performance_upper_bound():
    # Test with input just above the cap to ensure capping works and performance is acceptable
    codeflash_output = funcA(1001); result = codeflash_output # 77.3μs -> 71.3μs (8.45% faster)

def test_funcA_performance_medium():
    # Test with a medium input (500)
    codeflash_output = funcA(500); result = codeflash_output # 40.3μs -> 37.0μs (9.00% faster)
    split_result = result.split()

# ----------------------
# Miscellaneous/Robustness Cases
# ----------------------

def test_funcA_mutation_detection():
    # Ensure that any off-by-one error or wrong separator fails this test
    codeflash_output = funcA(4); result = codeflash_output # 2.94μs -> 2.46μs (19.5% faster)
# 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_zero():
    # Test with number = 0 (should return empty string)
    codeflash_output = funcA(0) # 2.33μs -> 781ns (199% faster)

def test_one():
    # Test with number = 1 (should return '0')
    codeflash_output = funcA(1) # 2.56μs -> 2.14μs (19.6% faster)

def test_small_number():
    # Test with small number
    codeflash_output = funcA(3) # 2.87μs -> 2.31μs (23.8% faster)

def test_typical_number():
    # Test with a typical number
    codeflash_output = funcA(10) # 3.37μs -> 2.89μs (16.7% faster)

def test_typical_number_as_string():
    # Ensure output is string, not list or other type
    codeflash_output = funcA(5); result = codeflash_output # 2.73μs -> 2.50μs (9.22% faster)

# ---------------------------
# 2. Edge Test Cases
# ---------------------------

def test_negative_number():
    # Negative input should return empty string (range(negative) yields nothing)
    codeflash_output = funcA(-5) # 2.12μs -> 1.52μs (39.5% faster)

def test_large_number_cap():
    # Input above 1000 should cap at 1000
    codeflash_output = funcA(1500); out = codeflash_output # 80.4μs -> 75.9μs (5.93% faster)
    # Should be numbers 0 to 999
    expected = " ".join(str(i) for i in range(1000))

def test_number_is_1000():
    # Input exactly 1000 (edge cap)
    codeflash_output = funcA(1000); out = codeflash_output # 77.3μs -> 71.5μs (8.22% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_number_is_999():
    # Input just below cap
    codeflash_output = funcA(999); out = codeflash_output # 77.6μs -> 71.2μs (9.05% faster)
    expected = " ".join(str(i) for i in range(999))

def test_number_is_1001():
    # Input just above cap
    codeflash_output = funcA(1001); out = codeflash_output # 77.2μs -> 70.8μs (9.09% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_float_input():
    # Floats should raise TypeError (range expects int)
    with pytest.raises(TypeError):
        funcA(5.5)

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

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

def test_boolean_input():
    # True is treated as 1, False as 0
    codeflash_output = funcA(True) # 3.04μs -> 2.46μs (23.5% faster)
    codeflash_output = funcA(False) # 1.30μs -> 441ns (195% faster)

def test_large_negative():
    # Very large negative should return empty string
    codeflash_output = funcA(-1000000) # 2.48μs -> 1.64μs (51.2% faster)

def test_input_is_list():
    # List input should raise TypeError
    with pytest.raises(TypeError):
        funcA([5])

def test_input_is_dict():
    # Dict input should raise TypeError
    with pytest.raises(TypeError):
        funcA({'number': 5})

# ---------------------------
# 3. Large Scale Test Cases
# ---------------------------

def test_large_scale_just_under_cap():
    # Test with 999 elements
    codeflash_output = funcA(999); result = codeflash_output # 80.8μs -> 73.7μs (9.69% faster)
    parts = result.split()
    # Ensure all numbers are present and in order
    for i, val in enumerate(parts):
        pass

def test_large_scale_cap():
    # Test with exactly 1000 elements
    codeflash_output = funcA(1000); result = codeflash_output # 78.3μs -> 71.5μs (9.40% faster)
    parts = result.split()
    # Ensure all numbers are present and in order
    for i, val in enumerate(parts):
        pass

def test_large_scale_above_cap():
    # Test with number above cap (e.g., 1234)
    codeflash_output = funcA(1234); result = codeflash_output # 77.7μs -> 71.5μs (8.66% faster)
    parts = result.split()
    # Ensure all numbers are present and in order
    for i, val in enumerate(parts):
        pass

def test_performance_large_input():
    # Test that function executes quickly for large input (under 1000 elements)
    import time
    start = time.time()
    codeflash_output = funcA(1000); result = codeflash_output # 77.3μs -> 71.2μs (8.56% faster)
    end = time.time()

# ---------------------------
# Mutation Testing Sensitivity
# ---------------------------

def test_mutation_no_extra_spaces():
    # Should not have trailing or leading spaces
    codeflash_output = funcA(10); result = codeflash_output # 3.48μs -> 3.12μs (11.6% faster)

def test_mutation_order():
    # Should be in ascending order
    codeflash_output = funcA(10); result = codeflash_output # 3.40μs -> 2.97μs (14.5% faster)
    parts = result.split()

def test_mutation_no_skipped_numbers():
    # No skipped numbers in output
    n = 100
    codeflash_output = funcA(n); result = codeflash_output # 10.3μs -> 9.36μs (10.1% faster)
    parts = result.split()
    for i in range(n):
        pass
# 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-mccvvlhv and push.