Datasets:
Tasks:
Text Classification
Formats:
parquet
Sub-tasks:
semantic-similarity-classification
Languages:
code
Size:
1M - 10M
License:
| annotations_creators: | |
| - found | |
| language_creators: | |
| - found | |
| language: | |
| - code | |
| license: | |
| - c-uda | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 1M<n<10M | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - semantic-similarity-classification | |
| pretty_name: CodeXGlueCcCloneDetectionBigCloneBench | |
| dataset_info: | |
| features: | |
| - name: id | |
| dtype: int32 | |
| - name: id1 | |
| dtype: int32 | |
| - name: id2 | |
| dtype: int32 | |
| - name: func1 | |
| dtype: string | |
| - name: func2 | |
| dtype: string | |
| - name: label | |
| dtype: bool | |
| splits: | |
| - name: train | |
| num_bytes: 2888035029 | |
| num_examples: 901028 | |
| - name: validation | |
| num_bytes: 1371399358 | |
| num_examples: 415416 | |
| - name: test | |
| num_bytes: 1220662565 | |
| num_examples: 415416 | |
| download_size: 1279275281 | |
| dataset_size: 5480096952 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| - split: validation | |
| path: data/validation-* | |
| - split: test | |
| path: data/test-* | |
| # Dataset Card for "code_x_glue_cc_clone_detection_big_clone_bench" | |
| ## 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/Clone-detection-BigCloneBench | |
| ### Dataset Summary | |
| CodeXGLUE Clone-detection-BigCloneBench dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench | |
| Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score. | |
| The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree. | |
| ### Supported Tasks and Leaderboards | |
| - `semantic-similarity-classification`: The dataset can be used to train a model for classifying if two given java methods are cloens of each other. | |
| ### Languages | |
| - Java **programming** language | |
| ## Dataset Structure | |
| ### Data Instances | |
| An example of 'test' looks as follows. | |
| ``` | |
| { | |
| "func1": " @Test(expected = GadgetException.class)\n public void malformedGadgetSpecIsCachedAndThrows() throws Exception {\n HttpRequest request = createCacheableRequest();\n expect(pipeline.execute(request)).andReturn(new HttpResponse(\"malformed junk\")).once();\n replay(pipeline);\n try {\n specFactory.getGadgetSpec(createContext(SPEC_URL, false));\n fail(\"No exception thrown on bad parse\");\n } catch (GadgetException e) {\n }\n specFactory.getGadgetSpec(createContext(SPEC_URL, false));\n }\n", | |
| "func2": " public InputStream getInputStream() throws TGBrowserException {\n try {\n if (!this.isFolder()) {\n URL url = new URL(this.url);\n InputStream stream = url.openStream();\n return stream;\n }\n } catch (Throwable throwable) {\n throw new TGBrowserException(throwable);\n }\n return null;\n }\n", | |
| "id": 0, | |
| "id1": 2381663, | |
| "id2": 4458076, | |
| "label": false | |
| } | |
| ``` | |
| ### 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 | | |
| |id1 |int32 | The first function id | | |
| |id2 |int32 | The second function id | | |
| |func1 |string| The full text of the first function | | |
| |func2 |string| The full text of the second function | | |
| |label |bool | 1 is the functions are not equivalent, 0 otherwise| | |
| ### Data Splits | |
| | name |train |validation| test | | |
| |-------|-----:|---------:|-----:| | |
| |default|901028| 415416|415416| | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| [More Information Needed] | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| Data was mined from the IJaDataset 2.0 dataset. | |
| [More Information Needed] | |
| #### Who are the source language producers? | |
| [More Information Needed] | |
| ### Annotations | |
| #### Annotation process | |
| Data was manually labeled by three judges by automatically identifying potential clones using search heuristics. | |
| [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 | |
| Most of the clones are type 1 and 2 with type 3 and especially type 4 being rare. | |
| [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 | |
| ``` | |
| @inproceedings{svajlenko2014towards, | |
| title={Towards a big data curated benchmark of inter-project code clones}, | |
| author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun}, | |
| booktitle={2014 IEEE International Conference on Software Maintenance and Evolution}, | |
| pages={476--480}, | |
| year={2014}, | |
| organization={IEEE} | |
| } | |
| @inproceedings{wang2020detecting, | |
| title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree}, | |
| author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi}, | |
| booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)}, | |
| pages={261--271}, | |
| year={2020}, | |
| organization={IEEE} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to @madlag (and partly also @ncoop57) for adding this dataset. |