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README.md

@@ -1,212 +0,0 @@
----
-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: 2888035757
-    num_examples: 901028
-  - name: validation
-    num_bytes: 1371399694
-    num_examples: 415416
-  - name: test
-    num_bytes: 1220662901
-    num_examples: 415416
-  download_size: 47955874
-  dataset_size: 5480098352
----
-# 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.

code_x_glue_cc_clone_detection_big_clone_bench.py

@@ -1,95 +0,0 @@
-from typing import List
-
-import datasets
-
-from .common import TrainValidTestChild
-from .generated_definitions import DEFINITIONS
-
-
-_DESCRIPTION = """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."""
-
-_CITATION = """@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}
-}"""
-
-
-class CodeXGlueCcCloneDetectionBigCloneBenchImpl(TrainValidTestChild):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "id1": datasets.Value("int32"),  # The first function id
-        "id2": datasets.Value("int32"),  # The second function id
-        "func1": datasets.Value("string"),  # The full text of the first function
-        "func2": datasets.Value("string"),  # The full text of the second function
-        "label": datasets.Value("bool"),  # 1 is the functions are not equivalent, 0 otherwise
-    }
-
-    _SUPERVISED_KEYS = ["label"]
-
-    def generate_urls(self, split_name):
-        yield "index", f"{split_name}.txt"
-        yield "data", "data.jsonl"
-
-    def _generate_examples(self, split_name, file_paths):
-        import json
-
-        js_all = {}
-
-        with open(file_paths["data"], encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                entry = json.loads(line)
-                js_all[int(entry["idx"])] = entry["func"]
-
-        with open(file_paths["index"], encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                line = line.strip()
-                idx1, idx2, label = [int(i) for i in line.split("\t")]
-                func1 = js_all[idx1]
-                func2 = js_all[idx2]
-
-                yield idx, dict(id=idx, id1=idx1, id2=idx2, func1=func1, func2=func2, label=(label == 1))
-
-
-CLASS_MAPPING = {
-    "CodeXGlueCcCloneDetectionBigCloneBench": CodeXGlueCcCloneDetectionBigCloneBenchImpl,
-}
-
-
-class CodeXGlueCcCloneDetectionBigCloneBench(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 @@
-{"default": {"description": "CodeXGLUE Clone-detection-BigCloneBench dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench\n\nGiven 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.\nThe dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree.", "citation": "@inproceedings{svajlenko2014towards,\ntitle={Towards a big data curated benchmark of inter-project code clones},\nauthor={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},\nbooktitle={2014 IEEE International Conference on Software Maintenance and Evolution},\npages={476--480},\nyear={2014},\norganization={IEEE}\n}\n\n@inproceedings{wang2020detecting,\ntitle={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},\nauthor={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},\nbooktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},\npages={261--271},\nyear={2020},\norganization={IEEE}\n}", "homepage": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "id1": {"dtype": "int32", "id": null, "_type": "Value"}, "id2": {"dtype": "int32", "id": null, "_type": "Value"}, "func1": {"dtype": "string", "id": null, "_type": "Value"}, "func2": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "bool", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "label", "output": ""}, "task_templates": null, "builder_name": "code_x_glue_cc_clone_detection_big_clone_bench", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2888035757, "num_examples": 901028, "dataset_name": "code_x_glue_cc_clone_detection_big_clone_bench"}, "validation": {"name": "validation", "num_bytes": 1371399694, "num_examples": 415416, "dataset_name": "code_x_glue_cc_clone_detection_big_clone_bench"}, "test": {"name": "test", "num_bytes": 1220662901, "num_examples": 415416, "dataset_name": "code_x_glue_cc_clone_detection_big_clone_bench"}}, "download_checksums": {"https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Clone-detection-BigCloneBench/dataset/train.txt": {"num_bytes": 17043552, "checksum": "29119bfa94673374249c3424809fbe6baaa1f0e87a13e3c727bbd6cdf1224b77"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Clone-detection-BigCloneBench/dataset/data.jsonl": {"num_bytes": 15174797, "checksum": "d8bc51e62deddcc45bd26c5b57f5add2a2cf377f13b9f6c2fb656fbc8fca4dd2"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Clone-detection-BigCloneBench/dataset/valid.txt": {"num_bytes": 7861019, "checksum": "e59e8c1321df59b6ab0143165cb603030c55800c00e2d782e06810517b8de1e4"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Clone-detection-BigCloneBench/dataset/test.txt": {"num_bytes": 7876506, "checksum": "a6c0cf79be34e582fdc64007aa894ed094e4f9ff2e5395a8d2b5c39eeef2737a"}}, "download_size": 47955874, "post_processing_size": null, "dataset_size": 5480098352, "size_in_bytes": 5528054226}}

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generated_definitions.py

@@ -1,12 +0,0 @@
-DEFINITIONS = {
-    "default": {
-        "class_name": "CodeXGlueCcCloneDetectionBigCloneBench",
-        "dataset_type": "Code-Code",
-        "description": "CodeXGLUE Clone-detection-BigCloneBench dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench",
-        "dir_name": "Clone-detection-BigCloneBench",
-        "name": "default",
-        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench",
-        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Clone-detection-BigCloneBench/dataset",
-        "sizes": {"test": 415416, "train": 901028, "validation": 415416},
-    }
-}