Task Categories: text-classification
Languages: code
Size Categories: 10K<n<100K
Licenses: other-C-UDA
Language Creators: found
Annotations Creators: found
Source Datasets: original

Dataset Card for "code_x_glue_cc_defect_detection"

Dataset Summary

CodeXGLUE Defect-detection dataset, available at

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.


  • 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.


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]


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,

Licensing Information

Computational Use of Data Agreement (C-UDA) License.

Citation Information

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}


Thanks to @madlag (and partly also @ncoop57) for adding this dataset.

Models trained or fine-tuned on code_x_glue_cc_defect_detection

None yet