--- title: code_x_glue_cc_clone_detection_big_clone_bench canonical_url: "https://www.modelscope.cn/datasets/google/code_x_glue_cc_clone_detection_big_clone_bench" md_url: "https://www.modelscope.cn/datasets/google/code_x_glue_cc_clone_detection_big_clone_bench.md" repository: google/code_x_glue_cc_clone_detection_big_clone_bench last_updated: 2025-04-21 license: "Apache License 2.0" storage_size: "1.2 GB" downloads: 875 stars: 0 --- # code_x_glue_cc_clone_detection_big_clone_bench > code_x_glue_cc_clone_detection_big_clone_bench - google 在 ModelScope 开源的数据集。Dataset Card for "codexglueccclonedetectionbigclonebench" google/code_x_glue_cc_clone_detection_big_clone_bench 是 ModelScope 魔搭社区上的数据集，存储大小 1.2 GB，采用 Apache License 2.0 许可。 - **Repository**: google/code_x_glue_cc_clone_detection_big_clone_bench - **License**: Apache License 2.0 - **Storage size**: 1.2 GB - **Downloads**: 875 - **Stars**: 0 - **Last updated**: 2025-04-21 Source: https://www.modelscope.cn/datasets/google/code_x_glue_cc_clone_detection_big_clone_bench --- # 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.