code_x_glue_cc_code_refinement

References:

• Code
• Huggingface

medium

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:code_x_glue_cc_code_refinement/medium')

• Description:

CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement

We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length.

• License: No known license
• Version: 0.0.0
• Splits:

• Features:

{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "buggy": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "fixed": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

small

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:code_x_glue_cc_code_refinement/small')

• Description:

CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement

We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length.

• License: No known license
• Version: 0.0.0
• Splits:

• Features:

{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "buggy": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "fixed": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}