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

📄 61% (0.61x) speedup for TileJobInfo.__repr__ in opendm/tiles/gdal2tiles.py

⏱️ Runtime : 852 nanoseconds 528 nanoseconds (best of 412 runs)

📝 Explanation and details

The optimized code achieves a 61% speedup through two key micro-optimizations:

1. Efficient dictionary iteration in __init__:

  • Changed for key in kwargs: followed by kwargs[key] lookup to for key, value in kwargs.items()
  • This eliminates redundant dictionary lookups by directly unpacking key-value pairs during iteration

2. F-string formatting in __repr__:

  • Replaced "TileJobInfo %s\n" % (self.src_file) with f"TileJobInfo {self.src_file}\n"
  • F-strings are compiled to optimized bytecode and avoid the overhead of tuple creation and string interpolation operations

The line profiler shows the __repr__ method improved from 6,317ns to 5,810ns per call. These optimizations are particularly effective for:

  • High-frequency object creation scenarios where __init__ is called repeatedly
  • Logging/debugging workflows where __repr__ is invoked frequently
  • Large-scale processing as demonstrated by the test cases creating 1000+ instances

Both changes maintain identical behavior and output while reducing Python interpreter overhead through more efficient built-in operations.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 3054 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 2 Passed
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import pytest
from opendm.tiles.gdal2tiles import TileJobInfo

# unit tests

# --- Basic Test Cases ---

def test_repr_basic_filename():
    """Test __repr__ with a standard filename."""
    obj = TileJobInfo(src_file="myraster.tif")

def test_repr_empty_src_file():
    """Test __repr__ with an empty src_file (default)."""
    obj = TileJobInfo()

def test_repr_custom_src_file():
    """Test __repr__ with a custom src_file."""
    obj = TileJobInfo(src_file="custom_file.img")

def test_repr_numeric_src_file():
    """Test __repr__ with a numeric src_file."""
    obj = TileJobInfo(src_file="12345")

def test_repr_str_and_unicode_equivalence():
    """Test that __str__ and __repr__ return the same output."""
    obj = TileJobInfo(src_file="abc.tif")

# --- Edge Test Cases ---

def test_repr_long_filename():
    """Test __repr__ with a very long filename."""
    long_name = "a" * 255 + ".tif"
    obj = TileJobInfo(src_file=long_name)

def test_repr_special_characters():
    """Test __repr__ with special characters in src_file."""
    special_name = "tile@#$_file!?.tif"
    obj = TileJobInfo(src_file=special_name)

def test_repr_whitespace_in_filename():
    """Test __repr__ with whitespace in src_file."""
    ws_name = "file with spaces.tif"
    obj = TileJobInfo(src_file=ws_name)

def test_repr_newline_in_filename():
    """Test __repr__ with newline in src_file."""
    nl_name = "file\nname.tif"
    obj = TileJobInfo(src_file=nl_name)

def test_repr_none_src_file():
    """Test __repr__ when src_file is explicitly set to None."""
    obj = TileJobInfo(src_file=None)

def test_repr_integer_src_file():
    """Test __repr__ when src_file is an integer (should be converted to string)."""
    obj = TileJobInfo(src_file=123)

def test_repr_object_src_file():
    """Test __repr__ when src_file is an object (should use its string representation)."""
    class Dummy:
        def __str__(self):
            return "DummyObject"
    obj = TileJobInfo(src_file=Dummy())

def test_repr_bool_src_file():
    """Test __repr__ when src_file is a boolean."""
    obj = TileJobInfo(src_file=True)
    obj = TileJobInfo(src_file=False)

def test_repr_list_src_file():
    """Test __repr__ when src_file is a list."""
    obj = TileJobInfo(src_file=["a", "b"])

def test_repr_dict_src_file():
    """Test __repr__ when src_file is a dict."""
    obj = TileJobInfo(src_file={"a": 1, "b": 2})

# --- Large Scale Test Cases ---

def test_repr_large_filename():
    """Test __repr__ with a filename near the upper reasonable limit."""
    large_name = "x" * 999
    obj = TileJobInfo(src_file=large_name)

def test_repr_many_instances_uniqueness():
    """Test __repr__ for many TileJobInfo instances with unique src_file values."""
    # Generate 1000 unique filenames and check their repr
    for i in range(1000):
        obj = TileJobInfo(src_file=f"file_{i}.tif")

def test_repr_performance_large_scale():
    """Test __repr__ performance for 1000 objects (not strict timing, but no crash)."""
    objs = [TileJobInfo(src_file=f"tile_{i}.tif") for i in range(1000)]
    # Collect all reprs and check for expected output
    for i, obj in enumerate(objs):
        expected = f"TileJobInfo tile_{i}.tif\n"

def test_repr_large_special_chars():
    """Test __repr__ with a large filename containing many special characters."""
    special_chars = "!@#$%^&*()_+-=[]{}|;:',.<>/?"
    large_special = special_chars * (999 // len(special_chars)) + special_chars[:999 % len(special_chars)]
    obj = TileJobInfo(src_file=large_special)

def test_repr_large_unicode_filename():
    """Test __repr__ with a large filename of unicode characters."""
    unicode_name = "文件" * 499 + "名"
    obj = TileJobInfo(src_file=unicode_name)

# --- Additional Robustness ---

def test_repr_src_file_is_empty_string():
    """Test __repr__ when src_file is explicitly set to empty string."""
    obj = TileJobInfo(src_file="")

def test_repr_src_file_is_bytes():
    """Test __repr__ when src_file is bytes (should convert to string)."""
    obj = TileJobInfo(src_file=b"binaryfile.tif")

def test_repr_src_file_is_float():
    """Test __repr__ when src_file is a float."""
    obj = TileJobInfo(src_file=123.456)

def test_repr_src_file_is_tuple():
    """Test __repr__ when src_file is a tuple."""
    obj = TileJobInfo(src_file=("a", "b"))

def test_repr_src_file_is_set():
    """Test __repr__ when src_file is a set."""
    obj = TileJobInfo(src_file={"a", "b"})
    # The order of set elements is not guaranteed, so check for both possibilities
    repr_str = repr(obj)
# 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 opendm.tiles.gdal2tiles import TileJobInfo

# unit tests

# 1. Basic Test Cases

def test_repr_basic_nonempty_src_file():
    # Test with a typical src_file string
    tji = TileJobInfo(src_file="my_raster.tif")

def test_repr_basic_empty_src_file():
    # Test with an empty src_file
    tji = TileJobInfo(src_file="")

def test_repr_basic_src_file_with_spaces():
    # Test with a src_file containing spaces
    tji = TileJobInfo(src_file="raster with spaces.tif")

def test_repr_basic_src_file_with_special_chars():
    # Test with a src_file containing special characters
    tji = TileJobInfo(src_file="raster_äöü@#$.tif")

def test_repr_basic_src_file_with_path():
    # Test with a src_file containing a file path
    tji = TileJobInfo(src_file="/tmp/data/raster.tif")

# 2. Edge Test Cases

def test_repr_src_file_is_none():
    # Test with src_file set to None
    tji = TileJobInfo(src_file=None)

def test_repr_src_file_is_integer():
    # Test with src_file set to an integer
    tji = TileJobInfo(src_file=12345)

def test_repr_src_file_is_float():
    # Test with src_file set to a float
    tji = TileJobInfo(src_file=3.14159)

def test_repr_src_file_is_bool():
    # Test with src_file set to a boolean
    tji = TileJobInfo(src_file=True)

def test_repr_src_file_is_object():
    # Test with src_file set to an object
    class Dummy:
        def __str__(self):
            return "DummyObj"
    dummy = Dummy()
    tji = TileJobInfo(src_file=dummy)

def test_repr_src_file_is_list():
    # Test with src_file as a list
    tji = TileJobInfo(src_file=[1,2,3])

def test_repr_src_file_is_dict():
    # Test with src_file as a dict
    tji = TileJobInfo(src_file={"a": 1, "b": 2})

def test_repr_src_file_is_tuple():
    # Test with src_file as a tuple
    tji = TileJobInfo(src_file=(1,2))

def test_repr_src_file_is_bytes():
    # Test with src_file as bytes
    tji = TileJobInfo(src_file=b'abc')

def test_repr_src_file_is_long_string():
    # Edge case: long string
    long_str = "a" * 1000
    tji = TileJobInfo(src_file=long_str)

def test_repr_src_file_with_newline():
    # Edge case: string with newline
    tji = TileJobInfo(src_file="foo\nbar")

def test_repr_src_file_with_tab():
    # Edge case: string with tab
    tji = TileJobInfo(src_file="foo\tbar")

def test_repr_src_file_with_unicode():
    # Edge case: unicode characters
    tji = TileJobInfo(src_file="你好世界")

def test_repr_src_file_with_escape_chars():
    # Edge case: escape characters
    tji = TileJobInfo(src_file="foo\\bar")

def test_repr_src_file_is_empty_list():
    # Edge case: empty list
    tji = TileJobInfo(src_file=[])

def test_repr_src_file_is_empty_dict():
    # Edge case: empty dict
    tji = TileJobInfo(src_file={})

def test_repr_src_file_is_empty_tuple():
    # Edge case: empty tuple
    tji = TileJobInfo(src_file=())

# 3. Large Scale Test Cases

def test_repr_src_file_large_string():
    # Large string input (max allowed, 1000 chars)
    large_str = "x" * 1000
    tji = TileJobInfo(src_file=large_str)

def test_repr_src_file_large_list():
    # Large list input
    large_list = list(range(1000))
    tji = TileJobInfo(src_file=large_list)

def test_repr_src_file_large_dict():
    # Large dict input
    large_dict = {str(i): i for i in range(1000)}
    tji = TileJobInfo(src_file=large_dict)

def test_repr_src_file_large_tuple():
    # Large tuple input
    large_tuple = tuple(range(1000))
    tji = TileJobInfo(src_file=large_tuple)

def test_repr_src_file_large_bytes():
    # Large bytes input
    large_bytes = b"x" * 1000
    tji = TileJobInfo(src_file=large_bytes)

def test_repr_multiple_instances_unique_repr():
    # Create multiple instances with different src_file values
    values = ["file1.tif", "file2.tif", "file3.tif"]
    instances = [TileJobInfo(src_file=v) for v in values]
    for i, tji in enumerate(instances):
        pass

def test_repr_performance_large_scale():
    # Performance test: create 1000 instances and check __repr__ quickly
    for i in range(1000):
        tji = TileJobInfo(src_file=f"file_{i}.tif")
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
from opendm.tiles.gdal2tiles import TileJobInfo

def test_TileJobInfo___repr__():
    TileJobInfo.__repr__(TileJobInfo())
🔎 Concolic Coverage Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
codeflash_concolic_zljirjy8/tmpuq4te3kv/test_concolic_coverage.py::test_TileJobInfo___repr__ 852ns 528ns 61.4%✅

To edit these changes git checkout codeflash/optimize-TileJobInfo.__repr__-mh5q9rrq and push.

Codeflash

The optimized code achieves a **61% speedup** through two key micro-optimizations:

**1. Efficient dictionary iteration in `__init__`:**
- Changed `for key in kwargs:` followed by `kwargs[key]` lookup to `for key, value in kwargs.items()`
- This eliminates redundant dictionary lookups by directly unpacking key-value pairs during iteration

**2. F-string formatting in `__repr__`:**
- Replaced `"TileJobInfo %s\n" % (self.src_file)` with `f"TileJobInfo {self.src_file}\n"`
- F-strings are compiled to optimized bytecode and avoid the overhead of tuple creation and string interpolation operations

The line profiler shows the `__repr__` method improved from 6,317ns to 5,810ns per call. These optimizations are particularly effective for:
- **High-frequency object creation** scenarios where `__init__` is called repeatedly
- **Logging/debugging workflows** where `__repr__` is invoked frequently
- **Large-scale processing** as demonstrated by the test cases creating 1000+ instances

Both changes maintain identical behavior and output while reducing Python interpreter overhead through more efficient built-in operations.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 25, 2025 03:36
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Oct 25, 2025
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