Dataset Card for "code_x_glue_cc_cloze_testing_all"

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.