metadata
annotations_creators:
- found
language_creators:
- found
languages:
- code
licenses:
- other-C-UDA
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- slot-filling
pretty_name: CodeXGlueCcClozeTestingAll
configs:
- go
- java
- javascript
- php
- python
- ruby
Dataset Card for "code_x_glue_cc_cloze_testing_all"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
Dataset Summary
CodeXGLUE ClozeTesting-all dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-all
Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem. Here we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. Each instance in the dataset contains a masked code function, its docstring and the target word. The only difference between ClozeTest-maxmin and ClozeTest-all is their selected words sets, where ClozeTest-maxmin only contains two words while ClozeTest-all contains 930 words.
Supported Tasks and Leaderboards
slot-filling: The dataset can be used to train a model for predicting the missing token from a piece of code, similar to the Cloze test.
Languages
- Go programming language
- Java programming language
- Javascript programming language
- PHP programming language
- Python programming language
- Ruby programming language
Dataset Structure
Data Instances
go
An example of 'train' looks as follows.
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["MarshalJSON", "supports", "json", ".", "Marshaler", "interface"],
"pl_tokens": ["func", "(", "v", "ContextRealtimeData", ")", "MarshalJSON", "(", ")", "(", "[", "]", "byte", ",", "error", ")", "{", "w", ":=", "jwriter", ".", "<mask>", "{", "}", "\n", "easyjsonC5a4559bEncodeGithubComChromedpCdprotoWebaudio7", "(", "&", "w", ",", "v", ")", "\n", "return", "w", ".", "Buffer", ".", "BuildBytes", "(", ")", ",", "w", ".", "Error", "\n", "}"]
}
java
An example of 'train' looks as follows.
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["/", "*", "(", "non", "-", "Javadoc", ")"],
"pl_tokens": ["@", "Override", "public", "int", "peekBit", "(", ")", "throws", "AACException", "{", "int", "ret", ";", "if", "(", "bitsCached", ">", "0", ")", "{", "ret", "=", "(", "cache", ">>", "(", "bitsCached", "-", "1", ")", ")", "&", "1", ";", "}", "else", "{", "final", "int", "word", "=", "readCache", "(", "true", ")", ";", "ret", "=", "(", "<mask>", ">>", "WORD_BITS", "-", "1", ")", "&", "1", ";", "}", "return", "ret", ";", "}"]
}
javascript
An example of 'train' looks as follows.
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["Cast", "query", "params", "according", "to", "type"],
"pl_tokens": ["function", "castQueryParams", "(", "relId", ",", "data", ",", "{", "relationships", "}", ")", "{", "const", "relationship", "=", "relationships", "[", "relId", "]", "if", "(", "!", "relationship", ".", "query", ")", "{", "return", "{", "}", "}", "return", "Object", ".", "keys", "(", "relationship", ".", "query", ")", ".", "reduce", "(", "(", "params", ",", "<mask>", ")", "=>", "{", "const", "value", "=", "getField", "(", "data", ",", "relationship", ".", "query", "[", "key", "]", ")", "if", "(", "value", "===", "undefined", ")", "{", "throw", "new", "TypeError", "(", "'Missing value for query param'", ")", "}", "return", "{", "...", "params", ",", "[", "key", "]", ":", "value", "}", "}", ",", "{", "}", ")", "}"]
}
php
An example of 'train' looks as follows.
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["Get", "choices", "."],
"pl_tokens": ["protected", "<mask>", "getChoices", "(", "FormFieldTranslation", "$", "translation", ")", "{", "$", "choices", "=", "preg_split", "(", "'/\\r\\n|\\r|\\n/'", ",", "$", "translation", "->", "getOption", "(", "'choices'", ")", ",", "-", "1", ",", "PREG_SPLIT_NO_EMPTY", ")", ";", "return", "array_combine", "(", "$", "choices", ",", "$", "choices", ")", ";", "}"]
}
python
An example of 'train' looks as follows.
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["Post", "a", "review"],
"pl_tokens": ["def", "post_review", "(", "session", ",", "review", ")", ":", "# POST /api/projects/0.1/reviews/", "<mask>", "=", "make_post_request", "(", "session", ",", "'reviews'", ",", "json_data", "=", "review", ")", "json_data", "=", "response", ".", "json", "(", ")", "if", "response", ".", "status_code", "==", "200", ":", "return", "json_data", "[", "'status'", "]", "else", ":", "raise", "ReviewNotPostedException", "(", "message", "=", "json_data", "[", "'message'", "]", ",", "error_code", "=", "json_data", "[", "'error_code'", "]", ",", "request_id", "=", "json_data", "[", "'request_id'", "]", ")"]
}
ruby
An example of 'train' looks as follows.
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["By", "default", "taskers", "don", "t", "see", "the", "flor", "variables", "in", "the", "execution", ".", "If", "include_vars", "or", "exclude_vars", "is", "present", "in", "the", "configuration", "of", "the", "tasker", "some", "or", "all", "of", "the", "variables", "are", "passed", "."],
"pl_tokens": ["def", "gather_vars", "(", "executor", ",", "tconf", ",", "message", ")", "# try to return before a potentially costly call to executor.vars(nid)", "return", "nil", "if", "(", "tconf", ".", "keys", "&", "%w[", "include_vars", "exclude_vars", "]", ")", ".", "empty?", "# default behaviour, don't pass variables to taskers", "iv", "=", "expand_filter", "(", "tconf", "[", "'include_vars'", "]", ")", "return", "nil", "if", "iv", "==", "false", "ev", "=", "expand_filter", "(", "tconf", "[", "'exclude_vars'", "]", ")", "return", "{", "}", "if", "ev", "==", "true", "vars", "=", "executor", ".", "vars", "(", "message", "[", "'nid'", "]", ")", "return", "vars", "if", "iv", "==", "true", "vars", "=", "vars", ".", "select", "{", "|", "k", ",", "v", "|", "var_match", "(", "k", ",", "iv", ")", "}", "if", "<mask>", "vars", "=", "vars", ".", "reject", "{", "|", "k", ",", "v", "|", "var_match", "(", "k", ",", "ev", ")", "}", "if", "ev", "vars", "end"]
}
Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
go, java, javascript, php, python, ruby
| field name | type | description |
|---|---|---|
| id | int32 | Index of the sample |
| idx | string | Original index in the dataset |
| nl_tokens | Sequence[string] | Natural language tokens |
| pl_tokens | Sequence[string] | Programming language tokens |
Data Splits
| name | train |
|---|---|
| go | 25282 |
| java | 40492 |
| javascript | 13837 |
| php | 51930 |
| python | 40137 |
| ruby | 4437 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
Data from CodeSearchNet Challenge dataset. [More Information Needed]
Who are the source language producers?
Software Engineering developers.
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
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: An Open Challenge for Code Intelligence},
journal={arXiv},
year={2020},
}
@article{feng2020codebert,
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others},
journal={arXiv preprint arXiv:2002.08155},
year={2020}
}
@article{husain2019codesearchnet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset.