Dataset Card for "code_x_glue_tc_nl_code_search_adv"

Dataset Summary

CodeXGLUE NL-code-search-Adv dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv

The dataset we use comes from CodeSearchNet and we filter the dataset as the following:

• Remove examples that codes cannot be parsed into an abstract syntax tree.
• Remove examples that #tokens of documents is < 3 or >256
• Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
• Remove examples that documents are not English.

Supported Tasks and Leaderboards

• document-retrieval: The dataset can be used to train a model for retrieving top-k codes from a given English natural language query.

Languages

• Python programming language
• English natural language

Dataset Structure

Data Instances

An example of 'validation' looks as follows.

{
    "argument_list": "", 
    "code": "def Func(arg_0, arg_1='.', arg_2=True, arg_3=False, **arg_4):\n    \"\"\"Downloads Dailymotion videos by URL.\n    \"\"\"\n\n    arg_5 = get_content(rebuilt_url(arg_0))\n    arg_6 = json.loads(match1(arg_5, r'qualities\":({.+?}),\"'))\n    arg_7 = match1(arg_5, r'\"video_title\"\\s*:\\s*\"([^\"]+)\"') or \\\n            match1(arg_5, r'\"title\"\\s*:\\s*\"([^\"]+)\"')\n    arg_7 = unicodize(arg_7)\n\n    for arg_8 in ['1080','720','480','380','240','144','auto']:\n        try:\n            arg_9 = arg_6[arg_8][1][\"url\"]\n            if arg_9:\n                break\n        except KeyError:\n            pass\n\n    arg_10, arg_11, arg_12 = url_info(arg_9)\n\n    print_info(site_info, arg_7, arg_10, arg_12)\n    if not arg_3:\n        download_urls([arg_9], arg_7, arg_11, arg_12, arg_1=arg_1, arg_2=arg_2)", 
    "code_tokens": ["def", "Func", "(", "arg_0", ",", "arg_1", "=", "'.'", ",", "arg_2", "=", "True", ",", "arg_3", "=", "False", ",", "**", "arg_4", ")", ":", "arg_5", "=", "get_content", "(", "rebuilt_url", "(", "arg_0", ")", ")", "arg_6", "=", "json", ".", "loads", "(", "match1", "(", "arg_5", ",", "r'qualities\":({.+?}),\"'", ")", ")", "arg_7", "=", "match1", "(", "arg_5", ",", "r'\"video_title\"\\s*:\\s*\"([^\"]+)\"'", ")", "or", "match1", "(", "arg_5", ",", "r'\"title\"\\s*:\\s*\"([^\"]+)\"'", ")", "arg_7", "=", "unicodize", "(", "arg_7", ")", "for", "arg_8", "in", "[", "'1080'", ",", "'720'", ",", "'480'", ",", "'380'", ",", "'240'", ",", "'144'", ",", "'auto'", "]", ":", "try", ":", "arg_9", "=", "arg_6", "[", "arg_8", "]", "[", "1", "]", "[", "\"url\"", "]", "if", "arg_9", ":", "break", "except", "KeyError", ":", "pass", "arg_10", ",", "arg_11", ",", "arg_12", "=", "url_info", "(", "arg_9", ")", "print_info", "(", "site_info", ",", "arg_7", ",", "arg_10", ",", "arg_12", ")", "if", "not", "arg_3", ":", "download_urls", "(", "[", "arg_9", "]", ",", "arg_7", ",", "arg_11", ",", "arg_12", ",", "arg_1", "=", "arg_1", ",", "arg_2", "=", "arg_2", ")"], 
    "docstring": "Downloads Dailymotion videos by URL.", 
    "docstring_summary": "Downloads Dailymotion videos by URL.", 
    "docstring_tokens": ["Downloads", "Dailymotion", "videos", "by", "URL", "."], 
    "func_name": "", 
    "id": 0, 
    "identifier": "dailymotion_download", 
    "language": "python", 
    "nwo": "soimort/you-get", 
    "original_string": "", 
    "parameters": "(url, output_dir='.', merge=True, info_only=False, **kwargs)", 
    "path": "src/you_get/extractors/dailymotion.py", 
    "repo": "", 
    "return_statement": "", 
    "score": 0.9997601509094238, 
    "sha": "b746ac01c9f39de94cac2d56f665285b0523b974", 
    "url": "https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/dailymotion.py#L13-L35"
}

Data Fields

In the following each data field in go is explained for each config. The data fields are the same among all splits.

default

field name	type	description
id	int32	Index of the sample
repo	string	repo: the owner/repo
path	string	path: the full path to the original file
func_name	string	func_name: the function or method name
original_string	string	original_string: the raw string before tokenization or parsing
language	string	language: the programming language
code	string	code/function: the part of the original_string that is code
code_tokens	Sequence[string]	code_tokens/function_tokens: tokenized version of code
docstring	string	docstring: the top-level comment or docstring, if it exists in the original string
docstring_tokens	Sequence[string]	docstring_tokens: tokenized version of docstring
sha	string	sha of the file
url	string	url of the file
docstring_summary	string	Summary of the docstring
parameters	string	parameters of the function
return_statement	string	return statement
argument_list	string	list of arguments of the function
identifier	string	identifier
nwo	string	nwo
score	datasets.Value("float"]	score for this search

Data Splits

name	train	validation	test
default	251820	9604	19210

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{DBLP:journals/corr/abs-2102-04664,
  author    = {Shuai Lu and
               Daya Guo and
               Shuo Ren and
               Junjie Huang and
               Alexey Svyatkovskiy and
               Ambrosio Blanco and
               Colin B. Clement and
               Dawn Drain and
               Daxin Jiang and
               Duyu Tang and
               Ge Li and
               Lidong Zhou and
               Linjun Shou and
               Long Zhou and
               Michele Tufano and
               Ming Gong and
               Ming Zhou and
               Nan Duan and
               Neel Sundaresan and
               Shao Kun Deng and
               Shengyu Fu and
               Shujie Liu},
  title     = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding
               and Generation},
  journal   = {CoRR},
  volume    = {abs/2102.04664},
  year      = {2021}
}
@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.