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README.md

@@ -1,192 +0,0 @@
----
-annotations_creators:
-- found
-language_creators:
-- found
-language:
-- code
-license:
-- c-uda
-multilinguality:
-- other-programming-languages
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- text-classification
-task_ids:
-- multi-class-classification
-pretty_name: CodeXGlueCcDefectDetection
-dataset_info:
-  features:
-  - name: id
-    dtype: int32
-  - name: func
-    dtype: string
-  - name: target
-    dtype: bool
-  - name: project
-    dtype: string
-  - name: commit_id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 45723487
-    num_examples: 21854
-  - name: validation
-    num_bytes: 5582545
-    num_examples: 2732
-  - name: test
-    num_bytes: 5646752
-    num_examples: 2732
-  download_size: 61685715
-  dataset_size: 56952784
----
-# 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

@@ -1,78 +0,0 @@
-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

@@ -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 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}}

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generated_definitions.py

@@ -1,12 +0,0 @@
-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},
-    }
-}