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Update files from the datasets library (from 1.8.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.8.0

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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bin.* filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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1
+ ---
2
+ annotations_creators:
3
+ - expert-generated
4
+ language_creators:
5
+ - found
6
+ languages:
7
+ - code
8
+ licenses:
9
+ - other-C-UDA
10
+ multilinguality:
11
+ - other-programming-languages
12
+ size_categories:
13
+ - 10K<n<100K
14
+ source_datasets:
15
+ - original
16
+ task_categories:
17
+ - conditional-text-generation
18
+ task_ids:
19
+ - conditional-text-generation-other-debugging
20
+ ---
21
+
22
+ # Dataset Card for "code_x_glue_cc_code_refinement"
23
+
24
+ ## Table of Contents
25
+ - [Dataset Description](#dataset-description)
26
+ - [Dataset Summary](#dataset-summary)
27
+ - [Supported Tasks and Leaderboards](#supported-tasks)
28
+ - [Languages](#languages)
29
+ - [Dataset Structure](#dataset-structure)
30
+ - [Data Instances](#data-instances)
31
+ - [Data Fields](#data-fields)
32
+ - [Data Splits](#data-splits-sample-size)
33
+ - [Dataset Creation](#dataset-creation)
34
+ - [Curation Rationale](#curation-rationale)
35
+ - [Source Data](#source-data)
36
+ - [Annotations](#annotations)
37
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
38
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
39
+ - [Social Impact of Dataset](#social-impact-of-dataset)
40
+ - [Discussion of Biases](#discussion-of-biases)
41
+ - [Other Known Limitations](#other-known-limitations)
42
+ - [Additional Information](#additional-information)
43
+ - [Dataset Curators](#dataset-curators)
44
+ - [Licensing Information](#licensing-information)
45
+ - [Citation Information](#citation-information)
46
+ - [Contributions](#contributions)
47
+
48
+ ## Dataset Description
49
+
50
+ - **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement
51
+
52
+ ### Dataset Summary
53
+
54
+ CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement
55
+
56
+ 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.
57
+
58
+ ### Supported Tasks and Leaderboards
59
+
60
+ - `conditional-text-generation-other-debugging`: The dataset can be used to train a model for automatically fixing buggy code.
61
+
62
+ ### Languages
63
+
64
+ - Java **programming** language
65
+
66
+ ## Dataset Structure
67
+
68
+ ### Data Instances
69
+
70
+ #### medium
71
+
72
+ An example of 'train' looks as follows.
73
+ ```
74
+ {
75
+ "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",
76
+ "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",
77
+ "id": 0
78
+ }
79
+ ```
80
+
81
+ #### small
82
+
83
+ An example of 'validation' looks as follows.
84
+ ```
85
+ {
86
+ "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",
87
+ "fixed": "public java.util.List < TYPE_1 > METHOD_1 ( ) { return VAR_1 ; } \n",
88
+ "id": 0
89
+ }
90
+ ```
91
+
92
+ ### Data Fields
93
+
94
+ In the following each data field in go is explained for each config. The data fields are the same among all splits.
95
+
96
+ #### medium, small
97
+
98
+ |field name| type | description |
99
+ |----------|------|--------------------------------|
100
+ |id |int32 | Index of the sample |
101
+ |buggy |string| The buggy version of the code |
102
+ |fixed |string| The correct version of the code|
103
+
104
+ ### Data Splits
105
+
106
+ | name |train|validation|test|
107
+ |------|----:|---------:|---:|
108
+ |medium|52364| 6546|6545|
109
+ |small |46680| 5835|5835|
110
+
111
+ ## Dataset Creation
112
+
113
+ ### Curation Rationale
114
+
115
+ [More Information Needed]
116
+
117
+ ### Source Data
118
+
119
+ #### Initial Data Collection and Normalization
120
+
121
+ Downloaded from GitHub Archive every public GitHub event between March 2011 and October 2017 and used the Google BigQuery APIs.
122
+ [More Information Needed]
123
+
124
+ #### Who are the source language producers?
125
+
126
+ Software Engineering developers.
127
+
128
+ ### Annotations
129
+
130
+ #### Annotation process
131
+
132
+ 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.
133
+
134
+ #### Who are the annotators?
135
+
136
+ Heuristics and the authors of the paper.
137
+
138
+ ### Personal and Sensitive Information
139
+
140
+ [More Information Needed]
141
+
142
+ ## Considerations for Using the Data
143
+
144
+ ### Social Impact of Dataset
145
+
146
+ [More Information Needed]
147
+
148
+ ### Discussion of Biases
149
+
150
+ [More Information Needed]
151
+
152
+ ### Other Known Limitations
153
+
154
+ [More Information Needed]
155
+
156
+ ## Additional Information
157
+
158
+ ### Dataset Curators
159
+
160
+ https://github.com/microsoft, https://github.com/madlag
161
+
162
+ ### Licensing Information
163
+
164
+ Computational Use of Data Agreement (C-UDA) License.
165
+
166
+ ### Citation Information
167
+
168
+ ```
169
+ @article{CodeXGLUE,
170
+ title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
171
+ year={2020},}
172
+ ```
173
+
174
+ ### Contributions
175
+
176
+ Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
code_x_glue_cc_code_refinement.py ADDED
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1
+ from typing import List
2
+
3
+ import datasets
4
+
5
+ from .common import TrainValidTestChild
6
+ from .generated_definitions import DEFINITIONS
7
+
8
+
9
+ _DESCRIPTION = """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."""
10
+ _CITATION = """@article{10.1145/3340544,
11
+ author = {Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},
12
+ title = {An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation},
13
+ year = {2019},
14
+ issue_date = {October 2019},
15
+ publisher = {Association for Computing Machinery},
16
+ address = {New York, NY, USA},
17
+ volume = {28},
18
+ number = {4},
19
+ issn = {1049-331X},
20
+ url = {https://doi-org.proxy.wm.edu/10.1145/3340544},
21
+ doi = {10.1145/3340544},
22
+ abstract = {Millions of open source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. First, we mine millions of bug-fixes from the change histories of projects hosted on GitHub in order to extract meaningful examples of such bug-fixes. Next, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. In our empirical investigation, we found that such a model is able to fix thousands of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9--50% of the cases, depending on the number of candidate patches we allow it to generate. Also, the model is able to emulate a variety of different Abstract Syntax Tree operations and generate candidate patches in a split second.},
23
+ journal = {ACM Trans. Softw. Eng. Methodol.},
24
+ month = sep,
25
+ articleno = {19},
26
+ numpages = {29},
27
+ keywords = {bug-fixes, Neural machine translation}
28
+ }"""
29
+
30
+
31
+ class CodeXGlueCcCodeRefinementImpl(TrainValidTestChild):
32
+ _DESCRIPTION = _DESCRIPTION
33
+ _CITATION = _CITATION
34
+
35
+ _FEATURES = {
36
+ "id": datasets.Value("int32"), # Index of the sample
37
+ "buggy": datasets.Value("string"), # The buggy version of the code
38
+ "fixed": datasets.Value("string"), # The correct version of the code
39
+ }
40
+
41
+ _SUPERVISED_KEYS = ["fixed"]
42
+
43
+ def generate_urls(self, split_name):
44
+ size = self.info["parameters"]["size"]
45
+ for key in "buggy", "fixed":
46
+ yield key, f"{size}/{split_name}.buggy-fixed.{key}"
47
+
48
+ def _generate_examples(self, split_name, file_paths):
49
+ """This function returns the examples in the raw (text) form."""
50
+ # Open each file (one for java, and one for c#)
51
+ files = {k: open(file_paths[k], encoding="utf-8") for k in file_paths}
52
+
53
+ id_ = 0
54
+ while True:
55
+ # Read a single line from each file
56
+ entries = {k: files[k].readline() for k in file_paths}
57
+
58
+ empty = self.check_empty(entries)
59
+ if empty:
60
+ # We are done: end of files
61
+ return
62
+
63
+ entries["id"] = id_
64
+ yield id_, entries
65
+ id_ += 1
66
+
67
+
68
+ CLASS_MAPPING = {
69
+ "CodeXGlueCcCodeRefinement": CodeXGlueCcCodeRefinementImpl,
70
+ }
71
+
72
+
73
+ class CodeXGlueCcCodeRefinement(datasets.GeneratorBasedBuilder):
74
+ BUILDER_CONFIG_CLASS = datasets.BuilderConfig
75
+ BUILDER_CONFIGS = [
76
+ datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items()
77
+ ]
78
+
79
+ def _info(self):
80
+ name = self.config.name
81
+ info = DEFINITIONS[name]
82
+ if info["class_name"] in CLASS_MAPPING:
83
+ self.child = CLASS_MAPPING[info["class_name"]](info)
84
+ else:
85
+ raise RuntimeError(f"Unknown python class for dataset configuration {name}")
86
+ ret = self.child._info()
87
+ return ret
88
+
89
+ def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
90
+ return self.child._split_generators(dl_manager=dl_manager)
91
+
92
+ def _generate_examples(self, split_name, file_paths):
93
+ return self.child._generate_examples(split_name, file_paths)
common.py ADDED
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1
+ from typing import List
2
+
3
+ import datasets
4
+
5
+
6
+ # Citation, taken from https://github.com/microsoft/CodeXGLUE
7
+ _DEFAULT_CITATION = """@article{CodeXGLUE,
8
+ title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
9
+ year={2020},}"""
10
+
11
+
12
+ class Child:
13
+ _DESCRIPTION = None
14
+ _FEATURES = None
15
+ _CITATION = None
16
+ SPLITS = {"train": datasets.Split.TRAIN}
17
+ _SUPERVISED_KEYS = None
18
+
19
+ def __init__(self, info):
20
+ self.info = info
21
+
22
+ def homepage(self):
23
+ return self.info["project_url"]
24
+
25
+ def _info(self):
26
+ # This is the description that will appear on the datasets page.
27
+ return datasets.DatasetInfo(
28
+ description=self.info["description"] + "\n\n" + self._DESCRIPTION,
29
+ features=datasets.Features(self._FEATURES),
30
+ homepage=self.homepage(),
31
+ citation=self._CITATION or _DEFAULT_CITATION,
32
+ supervised_keys=self._SUPERVISED_KEYS,
33
+ )
34
+
35
+ def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
36
+ SPLITS = self.SPLITS
37
+ _URL = self.info["raw_url"]
38
+ urls_to_download = {}
39
+ for split in SPLITS:
40
+ if split not in urls_to_download:
41
+ urls_to_download[split] = {}
42
+
43
+ for key, url in self.generate_urls(split):
44
+ if not url.startswith("http"):
45
+ url = _URL + "/" + url
46
+ urls_to_download[split][key] = url
47
+
48
+ downloaded_files = {}
49
+ for k, v in urls_to_download.items():
50
+ downloaded_files[k] = dl_manager.download_and_extract(v)
51
+
52
+ return [
53
+ datasets.SplitGenerator(
54
+ name=SPLITS[k],
55
+ gen_kwargs={"split_name": k, "file_paths": downloaded_files[k]},
56
+ )
57
+ for k in SPLITS
58
+ ]
59
+
60
+ def check_empty(self, entries):
61
+ all_empty = all([v == "" for v in entries.values()])
62
+ all_non_empty = all([v != "" for v in entries.values()])
63
+
64
+ if not all_non_empty and not all_empty:
65
+ raise RuntimeError("Parallel data files should have the same number of lines.")
66
+
67
+ return all_empty
68
+
69
+
70
+ class TrainValidTestChild(Child):
71
+ SPLITS = {
72
+ "train": datasets.Split.TRAIN,
73
+ "valid": datasets.Split.VALIDATION,
74
+ "test": datasets.Split.TEST,
75
+ }
dataset_infos.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"medium": {"description": "CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement\n\nWe 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.", "citation": "@article{10.1145/3340544,\nauthor = {Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},\ntitle = {An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation},\nyear = {2019},\nissue_date = {October 2019},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nvolume = {28},\nnumber = {4},\nissn = {1049-331X},\nurl = {https://doi-org.proxy.wm.edu/10.1145/3340544},\ndoi = {10.1145/3340544},\nabstract = {Millions of open source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. First, we mine millions of bug-fixes from the change histories of projects hosted on GitHub in order to extract meaningful examples of such bug-fixes. Next, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. In our empirical investigation, we found that such a model is able to fix thousands of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9--50% of the cases, depending on the number of candidate patches we allow it to generate. Also, the model is able to emulate a variety of different Abstract Syntax Tree operations and generate candidate patches in a split second.},\njournal = {ACM Trans. Softw. Eng. Methodol.},\nmonth = sep,\narticleno = {19},\nnumpages = {29},\nkeywords = {bug-fixes, Neural machine translation}\n}", "homepage": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/code-refinement", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "buggy": {"dtype": "string", "id": null, "_type": "Value"}, "fixed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "fixed", "output": ""}, "task_templates": null, "builder_name": "code_x_glue_cc_code_refinement", "config_name": "medium", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 32614834, "num_examples": 52364, "dataset_name": "code_x_glue_cc_code_refinement"}, "validation": {"name": "validation", "num_bytes": 4086741, "num_examples": 6546, "dataset_name": "code_x_glue_cc_code_refinement"}, "test": {"name": "test", "num_bytes": 4063673, "num_examples": 6545, "dataset_name": "code_x_glue_cc_code_refinement"}}, "download_checksums": {"https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/medium/train.buggy-fixed.buggy": {"num_bytes": 16188348, "checksum": "4570731680fa183650864e8729a7354d235c9a3ef42f0085ace3441418074085"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/medium/train.buggy-fixed.fixed": {"num_bytes": 15798070, "checksum": "009c121662602642bc55f6882f220aea6a738e6a11f2c4df86e7fe3cd30c175c"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/medium/valid.buggy-fixed.buggy": {"num_bytes": 2028309, "checksum": "8ad01f88be2009599007f40427458d6d2601fe93f2f1d65b0f46b7d414a3add2"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/medium/valid.buggy-fixed.fixed": {"num_bytes": 1979872, "checksum": "7ef5e4b2e95914e0eceb4f2cf6dfad0641625145319b9836db70d3f8745ad2d6"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/medium/test.buggy-fixed.buggy": {"num_bytes": 2014594, "checksum": "21107528c3b25bfdec24d0c4c18a953de31c26f3795a7d7c9e108a60396bcd38"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/medium/test.buggy-fixed.fixed": {"num_bytes": 1970531, "checksum": "4b13298647e9a782bf908d4a26710e97a1846f5513a9bf1aa46ac8223fb84b3d"}}, "download_size": 39979724, "post_processing_size": null, "dataset_size": 40765248, "size_in_bytes": 80744972}, "small": {"description": "CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement\n\nWe 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.", "citation": "@article{10.1145/3340544,\nauthor = {Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},\ntitle = {An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation},\nyear = {2019},\nissue_date = {October 2019},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nvolume = {28},\nnumber = {4},\nissn = {1049-331X},\nurl = {https://doi-org.proxy.wm.edu/10.1145/3340544},\ndoi = {10.1145/3340544},\nabstract = {Millions of open source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. First, we mine millions of bug-fixes from the change histories of projects hosted on GitHub in order to extract meaningful examples of such bug-fixes. Next, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. In our empirical investigation, we found that such a model is able to fix thousands of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9--50% of the cases, depending on the number of candidate patches we allow it to generate. Also, the model is able to emulate a variety of different Abstract Syntax Tree operations and generate candidate patches in a split second.},\njournal = {ACM Trans. Softw. Eng. 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