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@codeflash-ai codeflash-ai bot commented Aug 31, 2025

📄 358% (3.58x) speedup for manga in src/async_examples/concurrency.py

⏱️ Runtime : 1.47 minutes 19.3 seconds (best of 16 runs)

📝 Explanation and details

The optimized code achieves a 357% speedup through two key optimizations:

1. Concurrent async execution with asyncio.gather():
The original code awaited each fake_api_call sequentially in a loop, causing 5 separate async waits. The optimized version creates all 5 tasks upfront and uses asyncio.gather(*tasks) to execute them concurrently. This reduces the total async wait time from ~5x the individual delay to just the single longest delay.

2. Compute-once optimization for expensive calculation:
The original code computed sum(range(100000)) inside the loop (5 times), which the profiler shows took 88.7% of the original runtime (1.35 seconds). The optimized version computes this once before the loop and reuses the result, reducing this expensive operation from 5 executions to 1.

Performance impact by line profiler:

  • Original: summer = sum(range(100000)) took 1.35s across 1,102 hits (in loop)
  • Optimized: Same operation took 0.29s with 222 hits (once outside loop)
  • The async gather operation efficiently parallelizes the I/O-bound fake API calls

Test case effectiveness:
The optimizations excel in scenarios with multiple async operations and repeated expensive computations. The timeout and cancellation tests particularly benefit from the reduced overall execution time, making the function more responsive to external control.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 222 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import asyncio  # used to run async functions
import time

import pytest  # used for our unit tests
from src.async_examples.concurrency import manga

# unit tests

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

@pytest.mark.asyncio
async def test_manga_returns_list_of_expected_length():
    """Test that manga returns a list of the correct length (2 entries per loop, 5 loops = 10)."""
    result = await manga()

@pytest.mark.asyncio
async def test_manga_returns_expected_strings():
    """Test that manga returns expected string patterns in its results."""
    result = await manga()
    # Check alternating pattern: even indices are processed async, odd are sync task
    for i in range(5):
        pass

@pytest.mark.asyncio
async def test_manga_is_awaitable():
    """Test that manga is an async function and must be awaited."""
    codeflash_output = manga(); coro = codeflash_output
    result = await coro

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

@pytest.mark.asyncio
async def test_manga_concurrent_execution():
    """Test that manga can be called concurrently and results are independent."""
    tasks = [manga(), manga()]
    results = await asyncio.gather(*tasks)

@pytest.mark.asyncio
async def test_manga_timeout_behavior():
    """Test manga's behavior when run with a timeout (should finish well within 1s)."""
    try:
        result = await asyncio.wait_for(manga(), timeout=1)
    except asyncio.TimeoutError:
        pytest.fail("manga() took too long and timed out unexpectedly")

@pytest.mark.asyncio
async def test_manga_cancellation():
    """Test that manga handles cancellation gracefully."""
    task = asyncio.create_task(manga())
    await asyncio.sleep(0.0002)  # Let it start
    task.cancel()
    try:
        await task
        pytest.fail("Cancelled manga() should raise asyncio.CancelledError")
    except asyncio.CancelledError:
        pass  # Expected

@pytest.mark.asyncio
async 
#------------------------------------------------
import asyncio  # used to run async functions
import time

import pytest  # used for our unit tests
from src.async_examples.concurrency import manga

# --- Unit Tests ---

# 1. Basic Test Cases

@pytest.mark.asyncio
async def test_manga_returns_expected_length():
    # Test that manga returns a list of 10 items (5 async, 5 sync)
    result = await manga()

@pytest.mark.asyncio
async def test_manga_content_format():
    # Test that the async and sync results are correctly formatted
    result = await manga()
    # Odd indices: sync results, Even indices: async results
    for i in range(5):
        pass

@pytest.mark.asyncio
async def test_manga_async_behavior():
    # Test that manga can be awaited and returns a coroutine
    codeflash_output = manga(); coro = codeflash_output
    result = await coro

# 2. Edge Test Cases

@pytest.mark.asyncio
async def test_manga_concurrent_execution():
    # Run several manga coroutines concurrently and check results
    tasks = [manga() for _ in range(3)]
    results = await asyncio.gather(*tasks)
    for result in results:
        pass

@pytest.mark.asyncio
async def test_manga_cancellation():
    # Test that manga can be cancelled mid-execution
    task = asyncio.create_task(manga())
    await asyncio.sleep(0.0002)  # Let it start
    task.cancel()
    try:
        await task
    except asyncio.CancelledError:
        pass  # Expected

@pytest.mark.asyncio
async 
#------------------------------------------------
from src.async_examples.concurrency import manga

To edit these changes git checkout codeflash/optimize-manga-mezye9xc and push.

Codeflash

The optimized code achieves a **357% speedup** through two key optimizations:

**1. Concurrent async execution with `asyncio.gather()`:**
The original code awaited each `fake_api_call` sequentially in a loop, causing 5 separate async waits. The optimized version creates all 5 tasks upfront and uses `asyncio.gather(*tasks)` to execute them concurrently. This reduces the total async wait time from ~5x the individual delay to just the single longest delay.

**2. Compute-once optimization for expensive calculation:**
The original code computed `sum(range(100000))` inside the loop (5 times), which the profiler shows took 88.7% of the original runtime (1.35 seconds). The optimized version computes this once before the loop and reuses the result, reducing this expensive operation from 5 executions to 1.

**Performance impact by line profiler:**
- Original: `summer = sum(range(100000))` took 1.35s across 1,102 hits (in loop)
- Optimized: Same operation took 0.29s with 222 hits (once outside loop)
- The async gather operation efficiently parallelizes the I/O-bound fake API calls

**Test case effectiveness:**
The optimizations excel in scenarios with multiple async operations and repeated expensive computations. The timeout and cancellation tests particularly benefit from the reduced overall execution time, making the function more responsive to external control.
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Aug 31, 2025
@codeflash-ai codeflash-ai bot requested a review from KRRT7 August 31, 2025 17:17
@KRRT7 KRRT7 closed this Sep 4, 2025
@codeflash-ai codeflash-ai bot deleted the codeflash/optimize-manga-mezye9xc branch September 4, 2025 17:50
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