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README.md

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----
-annotations_creators:
-- expert-generated
-language_creators:
-- found
-language:
-- code
-license:
-- c-uda
-multilinguality:
-- other-programming-languages
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- text2text-generation
-task_ids: []
-pretty_name: CodeXGlueCcCodeRefinement
-tags:
-- debugging
-dataset_info:
-- config_name: medium
-  features:
-  - name: id
-    dtype: int32
-  - name: buggy
-    dtype: string
-  - name: fixed
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 32614834
-    num_examples: 52364
-  - name: validation
-    num_bytes: 4086741
-    num_examples: 6546
-  - name: test
-    num_bytes: 4063673
-    num_examples: 6545
-  download_size: 39979724
-  dataset_size: 40765248
-- config_name: small
-  features:
-  - name: id
-    dtype: int32
-  - name: buggy
-    dtype: string
-  - name: fixed
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 13006719
-    num_examples: 46680
-  - name: validation
-    num_bytes: 1629250
-    num_examples: 5835
-  - name: test
-    num_bytes: 1619708
-    num_examples: 5835
-  download_size: 15555421
-  dataset_size: 16255677
----
-
-# Dataset Card for "code_x_glue_cc_code_refinement"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits-sample-size)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/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.

code_x_glue_cc_code_refinement.py

@@ -1,93 +0,0 @@
-from typing import List
-
-import datasets
-
-from .common import TrainValidTestChild
-from .generated_definitions import DEFINITIONS
-
-
-_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."""
-_CITATION = """@article{10.1145/3340544,
-author = {Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},
-title = {An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation},
-year = {2019},
-issue_date = {October 2019},
-publisher = {Association for Computing Machinery},
-address = {New York, NY, USA},
-volume = {28},
-number = {4},
-issn = {1049-331X},
-url = {https://doi-org.proxy.wm.edu/10.1145/3340544},
-doi = {10.1145/3340544},
-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.},
-journal = {ACM Trans. Softw. Eng. Methodol.},
-month = sep,
-articleno = {19},
-numpages = {29},
-keywords = {bug-fixes, Neural machine translation}
-}"""
-
-
-class CodeXGlueCcCodeRefinementImpl(TrainValidTestChild):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "buggy": datasets.Value("string"),  # The buggy version of the code
-        "fixed": datasets.Value("string"),  # The correct version of the code
-    }
-
-    _SUPERVISED_KEYS = ["fixed"]
-
-    def generate_urls(self, split_name):
-        size = self.info["parameters"]["size"]
-        for key in "buggy", "fixed":
-            yield key, f"{size}/{split_name}.buggy-fixed.{key}"
-
-    def _generate_examples(self, split_name, file_paths):
-        """This function returns the examples in the raw (text) form."""
-        # Open each file (one for java, and one for c#)
-        files = {k: open(file_paths[k], encoding="utf-8") for k in file_paths}
-
-        id_ = 0
-        while True:
-            # Read a single line from each file
-            entries = {k: files[k].readline() for k in file_paths}
-
-            empty = self.check_empty(entries)
-            if empty:
-                # We are done: end of files
-                return
-
-            entries["id"] = id_
-            yield id_, entries
-            id_ += 1
-
-
-CLASS_MAPPING = {
-    "CodeXGlueCcCodeRefinement": CodeXGlueCcCodeRefinementImpl,
-}
-
-
-class CodeXGlueCcCodeRefinement(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIG_CLASS = datasets.BuilderConfig
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items()
-    ]
-
-    def _info(self):
-        name = self.config.name
-        info = DEFINITIONS[name]
-        if info["class_name"] in CLASS_MAPPING:
-            self.child = CLASS_MAPPING[info["class_name"]](info)
-        else:
-            raise RuntimeError(f"Unknown python class for dataset configuration {name}")
-        ret = self.child._info()
-        return ret
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        return self.child._split_generators(dl_manager=dl_manager)
-
-    def _generate_examples(self, split_name, file_paths):
-        return self.child._generate_examples(split_name, file_paths)

common.py

@@ -1,75 +0,0 @@
-from typing import List
-
-import datasets
-
-
-# Citation, taken from https://github.com/microsoft/CodeXGLUE
-_DEFAULT_CITATION = """@article{CodeXGLUE,
-         title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
-         year={2020},}"""
-
-
-class Child:
-    _DESCRIPTION = None
-    _FEATURES = None
-    _CITATION = None
-    SPLITS = {"train": datasets.Split.TRAIN}
-    _SUPERVISED_KEYS = None
-
-    def __init__(self, info):
-        self.info = info
-
-    def homepage(self):
-        return self.info["project_url"]
-
-    def _info(self):
-        # This is the description that will appear on the datasets page.
-        return datasets.DatasetInfo(
-            description=self.info["description"] + "\n\n" + self._DESCRIPTION,
-            features=datasets.Features(self._FEATURES),
-            homepage=self.homepage(),
-            citation=self._CITATION or _DEFAULT_CITATION,
-            supervised_keys=self._SUPERVISED_KEYS,
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        SPLITS = self.SPLITS
-        _URL = self.info["raw_url"]
-        urls_to_download = {}
-        for split in SPLITS:
-            if split not in urls_to_download:
-                urls_to_download[split] = {}
-
-            for key, url in self.generate_urls(split):
-                if not url.startswith("http"):
-                    url = _URL + "/" + url
-                urls_to_download[split][key] = url
-
-        downloaded_files = {}
-        for k, v in urls_to_download.items():
-            downloaded_files[k] = dl_manager.download_and_extract(v)
-
-        return [
-            datasets.SplitGenerator(
-                name=SPLITS[k],
-                gen_kwargs={"split_name": k, "file_paths": downloaded_files[k]},
-            )
-            for k in SPLITS
-        ]
-
-    def check_empty(self, entries):
-        all_empty = all([v == "" for v in entries.values()])
-        all_non_empty = all([v != "" for v in entries.values()])
-
-        if not all_non_empty and not all_empty:
-            raise RuntimeError("Parallel data files should have the same number of lines.")
-
-        return all_empty
-
-
-class TrainValidTestChild(Child):
-    SPLITS = {
-        "train": datasets.Split.TRAIN,
-        "valid": datasets.Split.VALIDATION,
-        "test": datasets.Split.TEST,
-    }

dataset_infos.json

@@ -1 +0,0 @@
-{"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. 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": "small", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 13006719, "num_examples": 46680, "dataset_name": "code_x_glue_cc_code_refinement"}, "validation": {"name": "validation", "num_bytes": 1629250, "num_examples": 5835, "dataset_name": "code_x_glue_cc_code_refinement"}, "test": {"name": "test", "num_bytes": 1619708, "num_examples": 5835, "dataset_name": "code_x_glue_cc_code_refinement"}}, "download_checksums": {"https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/small/train.buggy-fixed.buggy": {"num_bytes": 6509949, "checksum": "dfb4366dedb73dd40f78c3af870ccb0a1aeff2d9ceb45585df26c99897740748"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/small/train.buggy-fixed.fixed": {"num_bytes": 5936570, "checksum": "c98b1139265d33e787a9dd742a464e7eb5bd137ebb3fcb54f0416ee7672739f3"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/small/valid.buggy-fixed.buggy": {"num_bytes": 815315, "checksum": "e83a0c524cdce5a4492dfe0c9bc7d642aa5ed267ddbae5ffd1e563fe54cae6b8"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/small/valid.buggy-fixed.fixed": {"num_bytes": 743907, "checksum": "dc9c23594350988dcdaf456a1e8eea1dc86c81d1c0ff9985ff6d63d15196ffd2"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/small/test.buggy-fixed.buggy": {"num_bytes": 809941, "checksum": "d2e675094f471b3bfbb9419eb7cf14dca11f8d41185319d3310cbc3f517df323"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/small/test.buggy-fixed.fixed": {"num_bytes": 739739, "checksum": "e7ec462d00d253ddec3d1cef7a06cfc99db1cfe94039a6607ed14afeef6be04a"}}, "download_size": 15555421, "post_processing_size": null, "dataset_size": 16255677, "size_in_bytes": 31811098}}

generated_definitions.py

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-        "class_name": "CodeXGlueCcCodeRefinement",
-        "dataset_type": "Code-Code",
-        "description": "CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement",
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-        "class_name": "CodeXGlueCcCodeRefinement",
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-        "sizes": {"test": 5835, "train": 46680, "validation": 5835},
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