| --- |
| 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: 32614786 |
| num_examples: 52364 |
| - name: validation |
| num_bytes: 4086733 |
| num_examples: 6546 |
| - name: test |
| num_bytes: 4063665 |
| num_examples: 6545 |
| download_size: 14929559 |
| dataset_size: 40765184 |
| - config_name: small |
| features: |
| - name: id |
| dtype: int32 |
| - name: buggy |
| dtype: string |
| - name: fixed |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 13006679 |
| num_examples: 46680 |
| - name: validation |
| num_bytes: 1629242 |
| num_examples: 5835 |
| - name: test |
| num_bytes: 1619700 |
| num_examples: 5835 |
| download_size: 5894462 |
| dataset_size: 16255621 |
| configs: |
| - config_name: medium |
| data_files: |
| - split: train |
| path: medium/train-* |
| - split: validation |
| path: medium/validation-* |
| - split: test |
| path: medium/test-* |
| - config_name: small |
| data_files: |
| - split: train |
| path: small/train-* |
| - split: validation |
| path: small/validation-* |
| - split: test |
| path: small/test-* |
| --- |
| |
| # 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 |
| - **Paper:** https://arxiv.org/abs/2102.04664 |
| |
| ### 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{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{tufano2019empirical, |
| title={An empirical study on learning bug-fixing patches in the wild via neural machine translation}, |
| author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys}, |
| journal={ACM Transactions on Software Engineering and Methodology (TOSEM)}, |
| volume={28}, |
| number={4}, |
| pages={1--29}, |
| year={2019}, |
| publisher={ACM New York, NY, USA} |
| } |
| ``` |
|
|
| ### Contributions |
|
|
| Thanks to @madlag (and partly also @ncoop57) for adding this dataset. |
