Error code: ConfigNamesError Exception: FileNotFoundError Message: Couldn't find a dataset script at /src/services/worker/semeru/code-code-CodeRefinement-Java-Small/code-code-CodeRefinement-Java-Small.py or any data file in the same directory. Couldn't find 'semeru/code-code-CodeRefinement-Java-Small' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in semeru/code-code-CodeRefinement-Java-Small. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 55, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /src/services/worker/semeru/code-code-CodeRefinement-Java-Small/code-code-CodeRefinement-Java-Small.py or any data file in the same directory. Couldn't find 'semeru/code-code-CodeRefinement-Java-Small' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in semeru/code-code-CodeRefinement-Java-Small.

Dataset is imported from CodeXGLUE and pre-processed using their script.

Where to find in Semeru:

The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/code-refinement/data/small in Semeru

Task Definition

Code refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers. In CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code. Models are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.

Dataset

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. This dataset is small.

Data Statistics

Data statistics of this dataset are shown in the below table:

	#Examples
	Small
Train	46,680
Valid	5,835
Test	5,835

Reference

@article{tufano2019empirical,
  title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},
  author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},
  journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},
  volume={28},
  number={4},
  pages={1--29},
  year={2019},
  publisher={ACM New York, NY, USA}
}