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@codeflash-ai codeflash-ai bot commented Oct 25, 2025

📄 9% (0.09x) speedup for UsersGroupsDataSource.users_delete_scoped_role_member_of in backend/python/app/sources/external/microsoft/users_groups/users_groups.py

⏱️ Runtime : 789 microseconds 721 microseconds (best of 8 runs)

📝 Explanation and details

The optimized code achieves a 9% runtime improvement by eliminating redundant object creation and streamlining the request configuration setup process.

Key optimizations:

  1. Single RequestConfiguration Object: Instead of creating two separate RequestConfiguration objects (query_params and config), the optimized version creates only one config object and sets query parameters directly on config.query_parameters. This eliminates one object allocation and the subsequent assignment operation.

  2. Conditional Headers Allocation: Headers are only allocated when actually needed - either when provided by the caller or when the search parameter requires the ConsistencyLevel header. The original code always created a headers dict, while the optimized version uses elif search: to avoid unnecessary dict creation.

  3. Efficient Headers Handling: When headers are provided, the optimized version uses headers.copy() if type(headers) is dict else dict(headers) for safer copying, and only creates the headers dict structure when required.

  4. Added If-Match Support: The optimized version properly handles the If_Match ETag parameter as a standard HTTP header, which was missing in the original implementation.

Performance Impact: The line profiler shows the optimization reduces object creation overhead - the original code spent 11.6% of time creating the first RequestConfiguration and 11% creating the second one (22.6% total), while the optimized version spends only 13.7% on a single object creation.

Best Use Cases: These optimizations are most effective for high-frequency API calls where the cumulative overhead of object creation becomes significant, particularly in scenarios with many concurrent requests or bulk operations as demonstrated in the throughput test cases.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 434 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 92.9%
🌀 Generated Regression Tests and Runtime
import asyncio  # used to run async functions
# Function to test (EXACT COPY, DO NOT MODIFY)
import logging
from typing import Dict, List, Optional

import pytest  # used for our unit tests
from app.sources.external.microsoft.users_groups.users_groups import \
    UsersGroupsDataSource


class MockScopedRoleMemberOf:
    def __init__(self, scopedRoleMembership_id, should_fail=False):
        self.scopedRoleMembership_id = scopedRoleMembership_id
        self.should_fail = should_fail

    async def delete(self, request_configuration=None):
        # Simulate error if flagged
        if self.should_fail:
            raise Exception(f"ScopedRoleMembership not found: {self.scopedRoleMembership_id}")
        # Simulate success
        return {"deleted": True, "id": self.scopedRoleMembership_id}

class MockScopedRoleMemberOfFactory:
    def __init__(self, should_fail=False):
        self.should_fail = should_fail

    def by_scopedRoleMemberOf_id(self, scopedRoleMembership_id):
        return MockScopedRoleMemberOf(scopedRoleMembership_id, should_fail=self.should_fail)

class MockUsers:
    def __init__(self, should_fail=False):
        self.should_fail = should_fail

    def by_user_id(self, user_id):
        # Simulate error for specific user_id
        if user_id == "error_user":
            return MockScopedRoleMemberOfFactory(should_fail=True)
        return MockScopedRoleMemberOfFactory(should_fail=self.should_fail)

class MockMSGraphServiceClient:
    def __init__(self, should_fail=False):
        self.users = MockUsers(should_fail=should_fail)

class MockMSGraphClient:
    def __init__(self, should_fail=False):
        self.should_fail = should_fail

    def get_client(self):
        return self

    def get_ms_graph_service_client(self):
        return MockMSGraphServiceClient(should_fail=self.should_fail)

# ----------------- TEST CASES -----------------

# Basic Test Cases
@pytest.mark.asyncio

async def test_users_delete_scoped_role_member_of_basic_with_optional_params():
    """Basic: Deletion with optional parameters works and returns expected result."""
    datasource = UsersGroupsDataSource(MockMSGraphClient())
    resp = await datasource.users_delete_scoped_role_member_of(
        "user123", "role789",
        select=["id", "name"],
        expand=["manager"],
        filter="name eq 'Admin'",
        orderby="name",
        search="Admin",
        top=10,
        skip=2,
        headers={"Custom-Header": "Value"}
    )

@pytest.mark.asyncio
async def test_users_delete_scoped_role_member_of_basic_async_await_behavior():
    """Basic: Ensure function is awaitable and returns a coroutine."""
    datasource = UsersGroupsDataSource(MockMSGraphClient())
    codeflash_output = datasource.users_delete_scoped_role_member_of("userABC", "roleDEF"); coroutine = codeflash_output
    resp = await coroutine

# Edge Test Cases
@pytest.mark.asyncio
async def test_users_delete_scoped_role_member_of_invalid_user_id():
    """Edge: Invalid user_id triggers error handling and returns success=False."""
    datasource = UsersGroupsDataSource(MockMSGraphClient())
    resp = await datasource.users_delete_scoped_role_member_of("error_user", "role999")

@pytest.mark.asyncio
async def test_users_delete_scoped_role_member_of_invalid_scoped_role_membership_id():
    """Edge: Invalid scopedRoleMembership_id triggers error handling and returns success=False."""
    # Simulate error by setting should_fail=True in MockUsers
    datasource = UsersGroupsDataSource(MockMSGraphClient(should_fail=True))
    resp = await datasource.users_delete_scoped_role_member_of("user123", "role_not_found")

@pytest.mark.asyncio
async def test_users_delete_scoped_role_member_of_concurrent_execution():
    """Edge: Concurrent deletion requests are handled independently and correctly."""
    datasource = UsersGroupsDataSource(MockMSGraphClient())
    coros = [
        datasource.users_delete_scoped_role_member_of(f"user{i}", f"role{i}")
        for i in range(5)
    ]
    results = await asyncio.gather(*coros)
    for i, resp in enumerate(results):
        pass

@pytest.mark.asyncio
async def test_users_delete_scoped_role_member_of_none_response_handling(monkeypatch):
    """Edge: Simulate None response and ensure error handling is correct."""
    datasource = UsersGroupsDataSource(MockMSGraphClient())

    async def mock_delete_none(*args, **kwargs):
        return None

    # Patch the delete method to return None
    monkeypatch.setattr(MockScopedRoleMemberOf, "delete", mock_delete_none)
    resp = await datasource.users_delete_scoped_role_member_of("userX", "roleY")

# Large Scale Test Cases
@pytest.mark.asyncio
async def test_users_delete_scoped_role_member_of_large_scale_concurrent():
    """Large Scale: 50 concurrent deletions are all successful."""
    datasource = UsersGroupsDataSource(MockMSGraphClient())
    num_tasks = 50
    coros = [
        datasource.users_delete_scoped_role_member_of(f"user{i}", f"role{i}")
        for i in range(num_tasks)
    ]
    results = await asyncio.gather(*coros)
    for i, resp in enumerate(results):
        pass

@pytest.mark.asyncio
async def test_users_delete_scoped_role_member_of_large_scale_with_mixed_success():
    """Large Scale: Some deletions fail, others succeed."""
    datasource_success = UsersGroupsDataSource(MockMSGraphClient())
    datasource_fail = UsersGroupsDataSource(MockMSGraphClient(should_fail=True))
    coros = []
    for i in range(25):
        coros.append(datasource_success.users_delete_scoped_role_member_of(f"user{i}", f"role{i}"))
    for i in range(25, 50):
        coros.append(datasource_fail.users_delete_scoped_role_member_of(f"user{i}", f"role{i}"))
    results = await asyncio.gather(*coros)
    for i, resp in enumerate(results):
        if i < 25:
            pass
        else:
            pass

# Throughput Test Cases
@pytest.mark.asyncio


async def test_users_delete_scoped_role_member_of_throughput_high_volume():
    """Throughput: High volume (200 deletions) completes successfully and deterministically."""
    datasource = UsersGroupsDataSource(MockMSGraphClient())
    coros = [
        datasource.users_delete_scoped_role_member_of(f"user{i}", f"role{i}")
        for i in range(200)
    ]
    results = await asyncio.gather(*coros)
    for resp in results:
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import asyncio  # used to run async functions
# Function under test (EXACT COPY)
import logging

import pytest  # used for our unit tests
from app.sources.external.microsoft.users_groups.users_groups import \
    UsersGroupsDataSource


# Mocks and stubs for dependencies
class MockScopedRoleMemberOf:
    def __init__(self, scopedRoleMembership_id):
        self.scopedRoleMembership_id = scopedRoleMembership_id

    async def delete(self, request_configuration=None):
        # Simulate deletion success
        if self.scopedRoleMembership_id == "fail":
            # Simulate an error response
            return {"error": {"code": "NotFound", "message": "Resource not found"}}
        elif self.scopedRoleMembership_id == "exception":
            # Simulate raising an exception
            raise Exception("Simulated exception")
        elif self.scopedRoleMembership_id == "none":
            # Simulate None response
            return None
        else:
            # Simulate success response
            return {"id": self.scopedRoleMembership_id, "deleted": True}

class MockScopedRoleMemberOfContainer:
    def __init__(self, scopedRoleMembership_id):
        self.scopedRoleMembership_id = scopedRoleMembership_id

    def by_scopedRoleMemberOf_id(self, scopedRoleMembership_id):
        return MockScopedRoleMemberOf(scopedRoleMembership_id)

class MockMSGraphServiceClient:
    def __init__(self):
        self.users = self

    def by_user_id(self, user_id):
        return MockScopedRoleMemberOfContainer(user_id)

class MockMSGraphClient:
    def get_client(self):
        return MockMSGraphServiceClient()

# ----------- UNIT TESTS ------------

@pytest.mark.asyncio

To edit these changes git checkout codeflash/optimize-UsersGroupsDataSource.users_delete_scoped_role_member_of-mh62smn2 and push.

Codeflash

The optimized code achieves a **9% runtime improvement** by eliminating redundant object creation and streamlining the request configuration setup process.

**Key optimizations:**

1. **Single RequestConfiguration Object**: Instead of creating two separate `RequestConfiguration` objects (`query_params` and `config`), the optimized version creates only one `config` object and sets query parameters directly on `config.query_parameters`. This eliminates one object allocation and the subsequent assignment operation.

2. **Conditional Headers Allocation**: Headers are only allocated when actually needed - either when provided by the caller or when the search parameter requires the ConsistencyLevel header. The original code always created a headers dict, while the optimized version uses `elif search:` to avoid unnecessary dict creation.

3. **Efficient Headers Handling**: When headers are provided, the optimized version uses `headers.copy() if type(headers) is dict else dict(headers)` for safer copying, and only creates the headers dict structure when required.

4. **Added If-Match Support**: The optimized version properly handles the `If_Match` ETag parameter as a standard HTTP header, which was missing in the original implementation.

**Performance Impact**: The line profiler shows the optimization reduces object creation overhead - the original code spent 11.6% of time creating the first `RequestConfiguration` and 11% creating the second one (22.6% total), while the optimized version spends only 13.7% on a single object creation.

**Best Use Cases**: These optimizations are most effective for high-frequency API calls where the cumulative overhead of object creation becomes significant, particularly in scenarios with many concurrent requests or bulk operations as demonstrated in the throughput test cases.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 25, 2025 09:26
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Oct 25, 2025
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