Update files from the datasets library (from 1.8.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.8.0

.gitattributes

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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- code
+licenses:
+- other-C-UDA
+multilinguality:
+- other-programming-languages
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+---
+# Dataset Card for "code_x_glue_cc_defect_detection"
+
+## 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/Defect-detection
+
+### Dataset Summary
+
+CodeXGLUE Defect-detection dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection
+
+Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
+The dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test.
+
+### Supported Tasks and Leaderboards
+
+- `multi-class-classification`: The dataset can be used to train a model for detecting if code has a defect in it.
+
+### Languages
+
+- C **programming** language
+
+## Dataset Structure
+
+### Data Instances
+
+An example of 'validation' looks as follows.
+```
+{
+    "commit_id": "aa1530dec499f7525d2ccaa0e3a876dc8089ed1e", 
+    "func": "static void filter_mirror_setup(NetFilterState *nf, Error **errp)\n{\n    MirrorState *s = FILTER_MIRROR(nf);\n    Chardev *chr;\n    chr = qemu_chr_find(s->outdev);\n    if (chr == NULL) {\n        error_set(errp, ERROR_CLASS_DEVICE_NOT_FOUND,\n                  \"Device '%s' not found\", s->outdev);\n    qemu_chr_fe_init(&s->chr_out, chr, errp);", 
+    "id": 8, 
+    "project": "qemu", 
+    "target": true
+}
+```
+
+### 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                      |
+|func      |string| The source code                          |
+|target    |bool  | 0 or 1 (vulnerability or not)            |
+|project   |string| Original project that contains this code |
+|commit_id |string| Commit identifier in the original project|
+
+### Data Splits
+
+| name  |train|validation|test|
+|-------|----:|---------:|---:|
+|default|21854|      2732|2732|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[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
+
+[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{zhou2019devign,
+title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
+author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
+booktitle={Advances in Neural Information Processing Systems},
+pages={10197--10207}, year={2019}
+```
+
+### Contributions
+
+Thanks to @madlag (and partly also @ncoop57) for adding this dataset.

code_x_glue_cc_defect_detection.py

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+from typing import List
+
+import datasets
+
+from .common import TrainValidTestChild
+from .generated_definitions import DEFINITIONS
+
+
+_DESCRIPTION = """Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
+The dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test."""
+_CITATION = """@inproceedings{zhou2019devign,
+title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
+author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
+booktitle={Advances in Neural Information Processing Systems},
+pages={10197--10207}, year={2019}"""
+
+
+class CodeXGlueCcDefectDetectionImpl(TrainValidTestChild):
+    _DESCRIPTION = _DESCRIPTION
+    _CITATION = _CITATION
+
+    _FEATURES = {
+        "id": datasets.Value("int32"),  # Index of the sample
+        "func": datasets.Value("string"),  # The source code
+        "target": datasets.Value("bool"),  # 0 or 1 (vulnerability or not)
+        "project": datasets.Value("string"),  # Original project that contains this code
+        "commit_id": datasets.Value("string"),  # Commit identifier in the original project
+    }
+    _SUPERVISED_KEYS = ["target"]
+
+    def generate_urls(self, split_name):
+        yield "index", f"{split_name}.txt"
+        yield "data", "function.json"
+
+    def _generate_examples(self, split_name, file_paths):
+        import json
+
+        js_all = json.load(open(file_paths["data"], encoding="utf-8"))
+
+        index = set()
+        with open(file_paths["index"], encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                index.add(int(line))
+
+        for idx, js in enumerate(js_all):
+            if idx in index:
+                js["id"] = idx
+                js["target"] = int(js["target"]) == 1
+                yield idx, js
+
+
+CLASS_MAPPING = {
+    "CodeXGlueCcDefectDetection": CodeXGlueCcDefectDetectionImpl,
+}
+
+
+class CodeXGlueCcDefectDetection(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

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+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

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+{"default": {"description": "CodeXGLUE Defect-detection dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection\n\nGiven a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.\nThe dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test.", "citation": "@inproceedings{zhou2019devign,\ntitle={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},\nauthor={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},\nbooktitle={Advances in Neural Information Processing Systems},\npages={10197--10207}, year={2019}", "homepage": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/Defect-detection", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "func": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"dtype": "bool", "id": null, "_type": "Value"}, "project": {"dtype": "string", "id": null, "_type": "Value"}, "commit_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "target", "output": ""}, "task_templates": null, "builder_name": "code_x_glue_cc_defect_detection", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 45723487, "num_examples": 21854, "dataset_name": "code_x_glue_cc_defect_detection"}, "validation": {"name": "validation", "num_bytes": 5582545, "num_examples": 2732, "dataset_name": "code_x_glue_cc_defect_detection"}, "test": {"name": "test", "num_bytes": 5646752, "num_examples": 2732, "dataset_name": "code_x_glue_cc_defect_detection"}}, "download_checksums": {"https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Defect-detection/dataset/train.txt": {"num_bytes": 122185, "checksum": "f0a25410594302a9f0e542a393ad82ad479308a8aa471f4d6cf61b91d6d572bf"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Defect-detection/dataset/function.json": {"num_bytes": 61532917, "checksum": "0a3b2d561dc6280e53795886ede727d0045c016d083905ba3e9ce384a7eab246"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Defect-detection/dataset/valid.txt": {"num_bytes": 15295, "checksum": "9f2fa1e108955f197d4a7fa2aa2c7f5e542457b51e0eb1f6e890172d6f700a6e"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Defect-detection/dataset/test.txt": {"num_bytes": 15318, "checksum": "b5336b337170ea1edf0570b69edb5a90e3c99bf41cd92909795f5fe32d376d52"}}, "download_size": 61685715, "post_processing_size": null, "dataset_size": 56952784, "size_in_bytes": 118638499}}

dummy/default/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d1d5e6ce75c2506167dcf500c1c29680db8946445e49db1b27345215cd6db2a0
+size 21872

generated_definitions.py

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+DEFINITIONS = {
+    "default": {
+        "class_name": "CodeXGlueCcDefectDetection",
+        "dataset_type": "Code-Code",
+        "description": "CodeXGLUE Defect-detection dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection",
+        "dir_name": "Defect-detection",
+        "name": "default",
+        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/Defect-detection",
+        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Defect-detection/dataset",
+        "sizes": {"test": 2732, "train": 21854, "validation": 2732},
+    }
+}