⚡️ Speed up function parse_log_matplotlib by 246%

[codeflash-ai]

📄 246% (2.46x) speedup for parse_log_matplotlib in evaluation/benchmarks/testgeneval/log_parsers.py

⏱️ Runtime : 4.56 milliseconds → 1.32 millisecond (best of 1077 runs)

📝 Explanation and details

Optimizations Made.

1. Replace Operation Optimization: The replace operation for MouseButton.LEFT and MouseButton.RIGHT is only executed when necessary by checking if the substrings are in the line. This effectively reduces repeated and potentially unnecessary operations.

2. Efficient Startswith Check: Instead of creating a list comprehension to check any startswith conditions, we utilize a dictionary (status_values) to contain TestStatus values. This method simplifies the loop and minimizes operation redundancy with early exits using break.

3. Efficient Memory Utilization: The program ensures minimal memory use by breaking early from the loop upon finding a matching test status, avoiding further unnecessary checks for each line.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 23 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	✅ 1 Passed
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests Details

from unittest.mock import MagicMock

# imports
import pytest  # used for our unit tests
# function to test
from evaluation.benchmarks.testgeneval.constants import TestStatus
from evaluation.benchmarks.testgeneval.log_parsers import parse_log_matplotlib

# unit tests

# Mock TestStatus to simulate the expected behavior for testing
TestStatus = MagicMock()
TestStatus.PASSED.value = "PASSED"
TestStatus.FAILED.value = "FAILED"
TestStatus.SKIPPED.value = "SKIPPED"

def test_single_passed_test_case():
    log = "PASSED test_case_1"
    expected_output = {"test_case_1": "PASSED"}
    codeflash_output = parse_log_matplotlib(log)

def test_single_failed_test_case():
    log = "FAILED - test_case_2"
    expected_output = {"test_case_2": "FAILED"}
    codeflash_output = parse_log_matplotlib(log)

def test_multiple_test_cases_mixed_statuses():
    log = "PASSED test_case_1\nFAILED test_case_2\nSKIPPED test_case_3"
    expected_output = {
        "test_case_1": "PASSED",
        "test_case_2": "FAILED",
        "test_case_3": "SKIPPED"
    }
    codeflash_output = parse_log_matplotlib(log)

def test_empty_log():
    log = ""
    expected_output = {}
    codeflash_output = parse_log_matplotlib(log)

def test_log_with_only_newlines():
    log = "\n\n\n"
    expected_output = {}
    codeflash_output = parse_log_matplotlib(log)

def test_log_with_mouse_button_events():
    log = "PASSED test_case_1\nMouseButton.LEFT test_case_2\nMouseButton.RIGHT test_case_3"
    expected_output = {
        "test_case_1": "PASSED",
        "test_case_2": "1",
        "test_case_3": "3"
    }
    codeflash_output = parse_log_matplotlib(log)

def test_log_with_irregular_whitespaces():
    log = "  PASSED   test_case_1  \nFAILED   -   test_case_2"
    expected_output = {
        "test_case_1": "PASSED",
        "test_case_2": "FAILED"
    }
    codeflash_output = parse_log_matplotlib(log)

def test_log_with_missing_status():
    log = "test_case_1"
    expected_output = {}
    codeflash_output = parse_log_matplotlib(log)

def test_log_with_missing_test_case_name():
    log = "PASSED"
    expected_output = {}
    codeflash_output = parse_log_matplotlib(log)

def test_large_number_of_test_cases():
    log = "PASSED test_case_1\n" * 1000 + "FAILED test_case_1001"
    expected_output = {"test_case_1": "PASSED"}
    expected_output.update({"test_case_1001": "FAILED"})
    codeflash_output = parse_log_matplotlib(log)

def test_log_with_long_test_case_names():
    log = "PASSED " + "test_case_" + "x" * 1000
    expected_output = {"test_case_" + "x" * 1000: "PASSED"}
    codeflash_output = parse_log_matplotlib(log)

def test_large_scale_mixed_statuses():
    log = "".join(f"PASSED test_case_{i}\n" for i in range(1000)) + "FAILED test_case_5001"
    expected_output = {f"test_case_{i}": "PASSED" for i in range(1000)}
    expected_output["test_case_5001"] = "FAILED"
    codeflash_output = parse_log_matplotlib(log)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from enum import Enum  # used to define the TestStatus enumeration

# imports
import pytest  # used for our unit tests
from evaluation.benchmarks.testgeneval.log_parsers import parse_log_matplotlib


# Mocking the TestStatus enumeration for testing purposes
class TestStatus(Enum):
    PASSED = "PASSED"
    FAILED = "FAILED"
    SKIPPED = "SKIPPED"
from evaluation.benchmarks.testgeneval.log_parsers import parse_log_matplotlib

# unit tests

def test_single_passed_case():
    # Test a single test case with PASSED status
    log = "PASSED test_case_1"
    expected = {"test_case_1": "PASSED"}
    codeflash_output = parse_log_matplotlib(log)

def test_single_failed_case():
    # Test a single test case with FAILED status
    log = "FAILED test_case_2"
    expected = {"test_case_2": "FAILED"}
    codeflash_output = parse_log_matplotlib(log)

def test_single_skipped_case():
    # Test a single test case with SKIPPED status
    log = "SKIPPED test_case_3"
    expected = {"test_case_3": "SKIPPED"}
    codeflash_output = parse_log_matplotlib(log)

def test_multiple_mixed_statuses():
    # Test multiple test cases with mixed statuses
    log = "PASSED test_case_1\nFAILED test_case_2\nSKIPPED test_case_3"
    expected = {
        "test_case_1": "PASSED",
        "test_case_2": "FAILED",
        "test_case_3": "SKIPPED"
    }
    codeflash_output = parse_log_matplotlib(log)

def test_empty_log():
    # Test an empty log
    log = ""
    expected = {}
    codeflash_output = parse_log_matplotlib(log)

def test_malformed_entries():
    # Test log with malformed entries
    log = "PASSED\nFAILED test_case_2\nSKIPPED"
    expected = {"test_case_2": "FAILED"}
    codeflash_output = parse_log_matplotlib(log)

def test_mouse_button_replacement():
    # Test log with mouse button replacements
    log = "PASSED MouseButton.LEFT test_case_4\nFAILED MouseButton.RIGHT test_case_5"
    expected = {
        "test_case_4": "PASSED",
        "test_case_5": "FAILED"
    }
    codeflash_output = parse_log_matplotlib(log)

def test_unexpected_status():
    # Test log with unexpected status
    log = "UNKNOWN test_case_6\nPASSED test_case_7"
    expected = {"test_case_7": "PASSED"}
    codeflash_output = parse_log_matplotlib(log)

def test_large_log():
    # Test a large log with many entries
    log = "\n".join(f"PASSED test_case_{i}" for i in range(1000))
    expected = {f"test_case_{i}": "PASSED" for i in range(1000)}
    codeflash_output = parse_log_matplotlib(log)

def test_minimum_viable_entry():
    # Test the minimum viable log entry
    log = "P t"
    expected = {"t": "P"}
    codeflash_output = parse_log_matplotlib(log)

def test_maximum_length_identifiers():
    # Test log with maximum length test case identifiers
    long_identifier = "x" * 1000
    log = f"PASSED {long_identifier}"
    expected = {long_identifier: "PASSED"}
    codeflash_output = parse_log_matplotlib(log)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from evaluation.benchmarks.testgeneval.log_parsers import parse_log_matplotlib

def test_parse_log_matplotlib():
    parse_log_matplotlib('\x00\nFAILED\x00')

To edit these changes git checkout codeflash/optimize-parse_log_matplotlib-m8wzffuf and push.