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

⚡️ This pull request contains optimizations for PR #553

If you approve this dependent PR, these changes will be merged into the original PR branch feat/markdown-read-writable-context.

This PR will be automatically closed if the original PR is merged.


📄 66% (0.66x) speedup for CodeStringsMarkdown.path_to_code_string in codeflash/models/models.py

⏱️ Runtime : 4.52 microseconds 2.72 microseconds (best of 33 runs)

📝 Explanation and details

Here is an optimized version of your program with improved runtime and memory usage. Your original path_to_code_string function uses a dictionary comprehension, which is already efficient, but we can further optimize by minimizing attribute lookups and potential object string conversions. Also, since the base class already stores attributes, we can annotate expected attribute types for better speed in static analysis and C extensions (not runtime, but helps readability and future optimization).

Here's the improved version.

Notes about the optimization:

  • The for-loop avoids repeated attribute lookups and is slightly faster and less memory-intensive than a dictionary comprehension in some cases (especially for larger datasets).
  • Converted self.code_strings to a local variable for faster access inside the loop.
  • No unnecessary temporary objects or function calls were introduced.
  • This also makes it easier to add future optimizations, such as slotting or generator-based approaches for extreme scale, if needed.

Performance justification:
This makes the method marginally faster for large code_strings collections because it reduces temporary object allocations and attribute lookups, and dictionary insertion in a loop is roughly the same speed as a comprehension but is more explicit for optimization.

Let me know if you need even lower-level optimization or have information about the structure of file_path or code that could allow further improvements!

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 6 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
from __future__ import annotations

from typing import Any

# imports
import pytest  # used for our unit tests
from codeflash.models.models import CodeStringsMarkdown
from pydantic import BaseModel

SPLITTER_MARKER = "# codeflash-splitter__"

# Minimal stub for get_code_block_splitter, since it's referenced in __str__ property
def get_code_block_splitter(file_path: str) -> str:
    return f"{SPLITTER_MARKER}{file_path}"

class CodeString(BaseModel):
    file_path: str
    code: str
from codeflash.models.models import CodeStringsMarkdown

# unit tests

# 1. Basic Test Cases

def test_empty_code_strings_returns_empty_dict():
    # Test that an empty code_strings list returns an empty dict
    csm = CodeStringsMarkdown(code_strings=[])
    codeflash_output = csm.path_to_code_string(); result = codeflash_output # 1.47μs -> 891ns (65.3% faster)


















from __future__ import annotations

from typing import Any

# imports
import pytest  # used for our unit tests
from codeflash.models.models import CodeStringsMarkdown
from pydantic import BaseModel

SPLITTER_MARKER = "# codeflash-splitter__"

def get_code_block_splitter(path: Any) -> str:
    # Dummy implementation for this test context
    return f"{SPLITTER_MARKER}{path}"

class CodeString(BaseModel):
    file_path: str
    code: str
from codeflash.models.models import CodeStringsMarkdown

# unit tests

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



def test_basic_empty_list():
    # Test with empty code_strings list
    csm = CodeStringsMarkdown(code_strings=[])
    codeflash_output = csm.path_to_code_string(); result = codeflash_output # 1.51μs -> 932ns (62.2% faster)
















def test_empty_codestring_object():
    # Test with completely default CodeStringsMarkdown (no code_strings)
    csm = CodeStringsMarkdown()
    codeflash_output = csm.path_to_code_string(); result = codeflash_output # 1.53μs -> 901ns (70.1% faster)

# -------- TEST NON-LIST CODE_STRINGS (should raise validation error) --------

To edit these changes git checkout codeflash/optimize-pr553-2025-07-25T13.33.19 and push.

Codeflash

…n PR #553 (`feat/markdown-read-writable-context`)

Here is an optimized version of your program with improved runtime and memory usage. Your original `path_to_code_string` function uses a dictionary comprehension, which is already efficient, but we can further optimize by minimizing attribute lookups and potential object string conversions. Also, since the base class already stores attributes, we can annotate expected attribute types for better speed in static analysis and C extensions (not runtime, but helps readability and future optimization). 

Here's the improved version.



**Notes about the optimization:**

- The for-loop avoids repeated attribute lookups and is slightly faster and less memory-intensive than a dictionary comprehension in some cases (especially for larger datasets).
- Converted `self.code_strings` to a local variable for faster access inside the loop.
- No unnecessary temporary objects or function calls were introduced.
- This also makes it easier to add future optimizations, such as slotting or generator-based approaches for extreme scale, if needed.

**Performance justification:**  
This makes the method marginally faster for large `code_strings` collections because it reduces temporary object allocations and attribute lookups, and dictionary insertion in a loop is roughly the same speed as a comprehension but is more explicit for optimization.

Let me know if you need even lower-level optimization or have information about the structure of `file_path` or `code` that could allow further improvements!
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Jul 25, 2025
@codeflash-ai codeflash-ai bot deleted the codeflash/optimize-pr553-2025-07-25T13.33.19 branch July 29, 2025 17:54
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