Issues on Python / Java tests for code generation

[Devy99]

Hi!
I would like to report some issues and possible improvements regarding a few code generation tasks for the Python and Java languages.

Python:

Task id: Python/9

The test function 'test_find_number_combinations' is never called in the program. So the tests are never executed.

Current implementation:

def test_find_number_combinations():
    # Call the function to get the combinations
    combinations = find_number_combinations()

    # Check that we have at least one valid combination
    assert len(combinations) > 0, "There should be at least one valid combination."

    # Iterate over each combination to perform further checks
    for combo in combinations:
            # Each combination should have exactly three numbers
        assert len(combo) == 3, "Each combination should have three numbers."

        # Check if numbers are 3-digit numbers
        for num in combo:
                assert 100 <= num <= 999, f"Each number should be a 3-digit number, got {num}."

        # Check the 1:2:3 ratio
        assert combo[1] == 2 * combo[0] and combo[2] == 3 * combo[0], "The numbers should be in a 1:2:3 ratio."

    print("All test cases passed!")

Potential fix:

def test_find_number_combinations():
     # Call the function to get the combinations
    combinations = find_number_combinations()

    # Check that we have at least one valid combination
    assert len(combinations) > 0, "There should be at least one valid combination."

    # Iterate over each combination to perform further checks
    for combo in combinations:
            # Each combination should have exactly three numbers
        assert len(combo) == 3, "Each combination should have three numbers."

        # Check if numbers are 3-digit numbers
        for num in combo:
                assert 100 <= num <= 999, f"Each number should be a 3-digit number, got {num}."

        # Check the 1:2:3 ratio
        assert combo[1] == 2 * combo[0] and combo[2] == 3 * combo[0], "The numbers should be in a 1:2:3 ratio."

    print("All test cases passed!")

# Run the test cases
test_find_number_combinations() # I've just added the function call here <------ FIX

Task id: Python/14

Not an issue, however, the proposed tests are very simple. Since examples of output are provided to the LLM in the docstring, the model could create a trivial if-else and thus pass the tests (by cheating). In this case, adding new unit tests different from those present in the docstring would make the test more robust.

Examples in the docstring:

"""
Examples:
 >>> verify_isbn("0-670-82162-4")
'Right'

>>> verify_isbn("0-670-82162-0")
'0-670-82162-4'
"""

Current tests:

def test_verify_isbn():
    # Test case 1: Correct ISBN number
    assert verify_isbn("0-670-82162-4") == "Right", "Test case 1 failed"

    # Test case 2: Incorrect ISBN number with wrong checksum digit
    assert verify_isbn("0-670-82162-0") == "0-670-82162-4", "Test case 2 failed"

    print("All test cases passed!")

# Run the test cases
test_verify_isbn()

Java

Task id: Java/53

The tests refer to a function "testReverseWords" that does not exist. Below, a potential fix:

Current implementation:

public static void main(String[] args) {
    testReverseWords("The quick brown fox", "ehT kciuq nworb xof");
    testReverseWords("Hello World", "olleH dlroW");
    testReverseWords("a b c d e f", "a b c d e f");
    System.out.println("All tests passed");
  }
}

Potential fix:

public static void main(String[] args) {
    assert reverseWords("The quick brown fox").equals("ehT kciuq nworb xof") : "Test failed for input: The quick brown fox";
    assert reverseWords("Hello World").equals("olleH dlroW") : "Test failed for input: Hello World";
    assert reverseWords("a b c d e f").equals("a b c d e f") : "Test failed for input: a b c d e f";
    System.out.println("All tests passed");
  }
}

Comments:

I use this opportunity to report also an issue related to the benchmark's replicability. Currently, using the benchmark is complicated and error-prone due to the poor documentation and example scripts that need to be completely readapted. To complete these tasks, I'm converting the datasets into a format that the MultiPL-E benchmark can understand and running them from there. It would be perfect to have a script and a Docker image that can automate this process like in MultiPL-E, or simply an extension of their tool that can directly handle other datasets.

[Challenging6]

Thank you very much for pointing out the data issue. We will update it with your corrections shortly. We also greatly appreciate your suggestion and will soon refer to multiple-E to optimize our evaluation process!