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@codeflash-ai codeflash-ai bot commented Jan 30, 2026

📄 7% (0.07x) speedup for bit_or in aerospike_helpers/operations/bitwise_operations.py

⏱️ Runtime : 307 microseconds 287 microseconds (best of 5 runs)

📝 Explanation and details

The optimized code achieves a 6% runtime improvement by caching the constant aerospike.OP_BIT_OR as a module-level variable _OP_BIT_OR. This eliminates repeated attribute lookups on the aerospike module object during each function call.

Key Change:

  • Added _OP_BIT_OR = aerospike.OP_BIT_OR at module level
  • Changed OP_KEY: aerospike.OP_BIT_OR to OP_KEY: _OP_BIT_OR in the returned dictionary

Why This Improves Performance:
In Python, attribute access (like aerospike.OP_BIT_OR) requires a dictionary lookup in the module's __dict__ at runtime. By caching this value once at module load time, each invocation of bit_or() avoids the attribute lookup overhead. The line profiler shows this optimization reduces time spent on the OP_KEY line from 315,754ns to 302,361ns (4% faster for that line alone).

Test Results Analysis:
The optimization provides consistent improvements across all test cases:

  • Simple cases show 13-52% speedups (e.g., test_basic_with_bytes: 52% faster)
  • The large-scale test with 500 operations shows a 5.7% improvement, demonstrating the cumulative benefit when bit_or() is called repeatedly
  • Edge cases with unusual inputs still benefit proportionally

Impact:
This optimization is particularly valuable when bit_or() is called frequently in hot paths or batch operations. The 6% overall speedup compounds significantly in scenarios involving hundreds or thousands of bitwise operations, as demonstrated by the large-scale test case showing consistent gains at scale.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 139 Passed
🌀 Generated Regression Tests 508 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
⚙️ Click to see Existing Unit Tests
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
test_bitwise_operations.py::TestBitwiseOperations.test_bit_or 1.93μs 1.71μs 13.2%✅
test_bitwise_operations.py::TestBitwiseOperations.test_bit_or_bad_arg 1.73μs 1.50μs 14.8%✅
test_bitwise_operations.py::TestBitwiseOperations.test_bit_or_bad_bin_name 1.70μs 1.57μs 8.21%✅
test_bitwise_operations.py::TestBitwiseOperations.test_bit_or_bit_offset_out_of_range 1.72μs 1.56μs 9.94%✅
test_bitwise_operations.py::TestBitwiseOperations.test_bit_or_bit_size_larger_than_value 1.84μs 1.57μs 17.0%✅
test_bitwise_operations.py::TestBitwiseOperations.test_bit_or_bit_size_too_large 1.77μs 1.54μs 15.0%✅
test_bitwise_operations.py::TestBitwiseOperations.test_bit_or_multiple_bytes 1.82μs 1.59μs 14.0%✅
test_bitwise_operations.py::TestBitwiseOperations.test_bit_or_multiple_bytes_value_unchanged 1.68μs 1.63μs 3.19%✅
🌀 Click to see Generated Regression Tests
import aerospike  # used because the function under test references aerospike.OP_BIT_OR
# imports
import pytest  # used for our unit tests
from aerospike_helpers.operations.bitwise_operations import bit_or

def test_basic_with_bytes():
    # Basic case: use bytes as the value and no policy provided (None).
    bin_name = "my_bin"  # typical bin name
    bit_offset = 0  # start at the beginning
    bit_size = 8  # operate on one byte worth of bits
    value_byte_size = 1  # length in bytes
    value = b"\x0f"  # a one-byte value
    codeflash_output = bit_or(bin_name, bit_offset, bit_size, value_byte_size, value, policy=None); result = codeflash_output # 3.53μs -> 2.32μs (52.0% faster)

    # The returned dictionary should contain only the expected keys (no extras).
    expected_keys = {"op", "bin", "policy", "bit_offset", "bit_size", "value_byte_size", "value"}

def test_basic_with_bytearray():
    # Basic case: use a bytearray (mutable) as the value and a policy dictionary.
    bin_name = "other_bin"
    bit_offset = 16
    bit_size = 12
    value_byte_size = 2
    value = bytearray(b"\xff\x00")  # two bytes in a bytearray
    policy = {"some_policy_key": "some_policy_value"}

    codeflash_output = bit_or(bin_name, bit_offset, bit_size, value_byte_size, value, policy=policy); result = codeflash_output # 2.87μs -> 2.01μs (42.8% faster)

def test_policy_identity_and_mutation_reflects_in_result():
    # Demonstrate that the function does not clone the policy dict - it preserves the same object.
    bin_name = "bin_policy"
    result_policy = {"initial": True}
    codeflash_output = bit_or(bin_name, 1, 1, 1, b"\x00", policy=result_policy); result = codeflash_output # 2.22μs -> 1.92μs (15.8% faster)

    # Mutate the original policy object and verify the returned dict reflects that mutation,
    # confirming that no defensive copy was made by the function.
    result_policy["added"] = 123

def test_edge_negative_and_zero_values_preserved():
    # Edge: Negative offsets and zero sizes are not validated by bit_or; function should preserve them.
    bin_name = "edge_bin"
    negative_offset = -5
    zero_size = 0
    value_byte_size = 0
    value = b""  # empty bytes

    codeflash_output = bit_or(bin_name, negative_offset, zero_size, value_byte_size, value, policy=None); result = codeflash_output # 2.22μs -> 1.84μs (20.7% faster)

def test_edge_unexpected_value_types_are_passed_through():
    # Edge: The function accepts arbitrary 'value' objects and simply stores them in the returned dict.
    # Test with a non-bytes type (e.g., integer and a tuple) to ensure pass-through behavior.
    bin_name = "weird_bin"
    integer_value = 42
    tuple_value = (1, 2, 3)

    codeflash_output = bit_or(bin_name, 0, 8, 1, integer_value, policy=None); res_int = codeflash_output # 2.08μs -> 1.83μs (13.4% faster)
    codeflash_output = bit_or(bin_name, 0, 24, 3, tuple_value, policy={"p": 1}); res_tuple = codeflash_output # 1.28μs -> 1.20μs (6.41% faster)

def test_value_byte_size_mismatch_is_preserved():
    # Edge: If value_byte_size does not match the actual length of value, the function should still preserve both fields.
    bin_name = "mismatch_bin"
    value = b"\x01"  # actual length = 1
    declared_size = 2  # mismatched declared value_byte_size

    codeflash_output = bit_or(bin_name, 8, 8, declared_size, value, policy=None); result = codeflash_output # 1.98μs -> 1.89μs (4.83% faster)

def test_large_scale_many_operations():
    # Large-scale: create many (but < 1000) bit_or operation dictionaries and verify consistency and performance.
    # We create 500 distinct operations to exercise potential scale issues without being excessively slow.
    count = 500
    results = []
    for i in range(count):
        # Vary parameters slightly for each operation.
        bin_name = f"bin_{i % 10}"  # reuse some bin names to simulate realistic usage
        bit_offset = i  # different offsets
        bit_size = (i % 64) + 1  # sizes in 1..64
        value_byte_size = (bit_size + 7) // 8  # minimal byte size that could hold bit_size bits
        # Create a small bytes payload derived from i; keep it tiny to avoid heavy memory usage.
        value = (i % 256).to_bytes(value_byte_size, byteorder="big")
        codeflash_output = bit_or(bin_name, bit_offset, bit_size, value_byte_size, value, policy=None); op = codeflash_output # 274μs -> 259μs (5.68% faster)
        results.append(op)

    # Sanity checks: every operation must have the correct op code and required keys.
    for idx, op in enumerate(results):
        pass

    # Confirm that there is at least some variety (i.e., not all bins are the same).
    bins = {op["bin"] for op in results}

def test_returned_dict_structure_is_exact_and_stable():
    # Ensure the exact structure and keys are stable and unchanged (mutation testing guard).
    # This helps catch mistakes where keys are renamed, omitted, or additional unexpected keys are added.
    codeflash_output = bit_or("stable_bin", 2, 4, 1, b"\x0f", policy={"k": "v"}); result = codeflash_output # 2.34μs -> 1.91μs (22.6% faster)

    # The expected set of keys must match exactly.
    expected_keys = {"op", "bin", "policy", "bit_offset", "bit_size", "value_byte_size", "value"}
# 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-bit_or-ml0k9u1r and push.

Codeflash Static Badge

The optimized code achieves a **6% runtime improvement** by caching the constant `aerospike.OP_BIT_OR` as a module-level variable `_OP_BIT_OR`. This eliminates repeated attribute lookups on the `aerospike` module object during each function call.

**Key Change:**
- Added `_OP_BIT_OR = aerospike.OP_BIT_OR` at module level
- Changed `OP_KEY: aerospike.OP_BIT_OR` to `OP_KEY: _OP_BIT_OR` in the returned dictionary

**Why This Improves Performance:**
In Python, attribute access (like `aerospike.OP_BIT_OR`) requires a dictionary lookup in the module's `__dict__` at runtime. By caching this value once at module load time, each invocation of `bit_or()` avoids the attribute lookup overhead. The line profiler shows this optimization reduces time spent on the `OP_KEY` line from 315,754ns to 302,361ns (4% faster for that line alone).

**Test Results Analysis:**
The optimization provides consistent improvements across all test cases:
- Simple cases show 13-52% speedups (e.g., `test_basic_with_bytes`: 52% faster)
- The large-scale test with 500 operations shows a 5.7% improvement, demonstrating the cumulative benefit when `bit_or()` is called repeatedly
- Edge cases with unusual inputs still benefit proportionally

**Impact:**
This optimization is particularly valuable when `bit_or()` is called frequently in hot paths or batch operations. The 6% overall speedup compounds significantly in scenarios involving hundreds or thousands of bitwise operations, as demonstrated by the large-scale test case showing consistent gains at scale.
@codeflash-ai codeflash-ai bot requested a review from aseembits93 January 30, 2026 07:28
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Jan 30, 2026
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⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash

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