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Error code: StreamingRowsError Exception: ValueError Message: Cannot seek streaming HTTP file Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/first_rows.py", line 571, in compute_first_rows_response rows = get_rows( File "/src/services/worker/src/worker/job_runners/first_rows.py", line 162, in decorator return func(*args, **kwargs) File "/src/services/worker/src/worker/job_runners/first_rows.py", line 205, in get_rows ds = load_dataset( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1775, in load_dataset return builder_instance.as_streaming_dataset(split=split) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1245, in as_streaming_dataset splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)} File "/tmp/modules-cache/datasets_modules/datasets/code_x_glue_cc_code_refinement/01a93c553626c3171fd010b39564cabbdabdfc395ed9abf8ceea9331d1c68eb4/code_x_glue_cc_code_refinement.py", line 90, in _split_generators return self.child._split_generators(dl_manager=dl_manager) File "/tmp/modules-cache/datasets_modules/datasets/code_x_glue_cc_code_refinement/01a93c553626c3171fd010b39564cabbdabdfc395ed9abf8ceea9331d1c68eb4/common.py", line 50, in _split_generators downloaded_files[k] = dl_manager.download_and_extract(v) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1074, in download_and_extract return self.extract(self.download(url_or_urls)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1026, in extract urlpaths = map_nested(self._extract, url_or_urls, map_tuple=True) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 443, in map_nested mapped = [ File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 444, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 346, in _single_map_nested return function(data_struct) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1031, in _extract protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 434, in _get_extraction_protocol return _get_extraction_protocol_with_magic_number(f) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 405, in _get_extraction_protocol_with_magic_number f.seek(0) File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 747, in seek raise ValueError("Cannot seek streaming HTTP file") ValueError: Cannot seek streaming HTTP file
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Dataset Card for "code_x_glue_cc_code_refinement"
Dataset Summary
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.
Supported Tasks and Leaderboards
text2text-generation-other-debugging
: The dataset can be used to train a model for automatically fixing buggy code.
Languages
- Java programming language
Dataset Structure
Data Instances
medium
An example of 'train' looks as follows.
{
"buggy": "public static TYPE_1 init ( java.lang.String name , java.util.Date date ) { TYPE_1 VAR_1 = new TYPE_1 ( ) ; VAR_1 . METHOD_1 ( name ) ; java.util.Calendar VAR_2 = java.util.Calendar.getInstance ( ) ; VAR_2 . METHOD_2 ( date ) ; VAR_1 . METHOD_3 ( VAR_2 ) ; return VAR_1 ; }\n",
"fixed": "public static TYPE_1 init ( java.lang.String name , java.util.Date date ) { TYPE_1 VAR_1 = new TYPE_1 ( ) ; VAR_1 . METHOD_1 ( name ) ; java.util.Calendar VAR_2 = null ; if ( date != null ) { VAR_2 = java.util.Calendar.getInstance ( ) ; VAR_2 . METHOD_2 ( date ) ; } VAR_1 . METHOD_3 ( VAR_2 ) ; return VAR_1 ; }\n",
"id": 0
}
small
An example of 'validation' looks as follows.
{
"buggy": "public java.util.List < TYPE_1 > METHOD_1 ( ) { java.util.ArrayList < TYPE_1 > VAR_1 = new java.util.ArrayList < TYPE_1 > ( ) ; for ( TYPE_2 VAR_2 : VAR_3 ) { VAR_1 . METHOD_2 ( VAR_2 . METHOD_1 ( ) ) ; } return VAR_1 ; } \n",
"fixed": "public java.util.List < TYPE_1 > METHOD_1 ( ) { return VAR_1 ; } \n",
"id": 0
}
Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
medium, small
field name | type | description |
---|---|---|
id | int32 | Index of the sample |
buggy | string | The buggy version of the code |
fixed | string | The correct version of the code |
Data Splits
name | train | validation | test |
---|---|---|---|
medium | 52364 | 6546 | 6545 |
small | 46680 | 5835 | 5835 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
Downloaded from GitHub Archive every public GitHub event between March 2011 and October 2017 and used the Google BigQuery APIs. [More Information Needed]
Who are the source language producers?
Software Engineering developers.
Annotations
Annotation process
Automatically annotated by filtering commit messages containing the pattern: ("fix" or "solve") and ("bug" or "issue" or "problem" or "error"). A statistically significant amount of samples (95% confidence level with 5% confidence interval) were manually evaluated by two authors to check if the filtered bug/fix pairs were correct. After all disagreements were settled, authors conclude that 97.6% were true positives.
Who are the annotators?
Heuristics and the authors of the paper.
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
https://github.com/microsoft, https://github.com/madlag
Licensing Information
Computational Use of Data Agreement (C-UDA) License.
Citation Information
@article{CodeXGLUE,
title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
year={2020},}
Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
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