
### Dataset is imported from CodeXGLUE and pre-processed using their script.

# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Defect-detection in Semeru



# CodeXGLUE -- Defect Detection

## Task Definition

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.

### Dataset

The dataset we use comes from the paper [*Devign*: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](http://papers.nips.cc/paper/9209-devign-effective-vulnerability-identification-by-learning-comprehensive-program-semantics-via-graph-neural-networks.pdf). We combine all projects and split 80%/10%/10% for training/dev/test.


### Data Format

Three pre-processed .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl are present

For each file, each line in the uncompressed file represents one function.  One row is illustrated below.

   - **func:** the source code
   - **target:** 0 or 1 (vulnerability or not)
   - **idx:** the index of example

### Data Statistics

Data statistics of the dataset are shown in the below table:

|       | #Examples |
| ----- | :-------: |
| Train |  21,854   |
| Dev   |   2,732   |
| Test  |   2,732   |


## Reference
<pre><code>@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}
}</code></pre>

license:mit region:us
{"license": "mit", "Programminglanguage": "C", "version": "N/A", "Date": "Devign(Jun 2019 - paper release date)", "Contaminated": "Very Likely", "Size": "Standard Tokenizer"}
TAGS
#license-mit #region-us 

### Dataset is imported from CodeXGLUE and pre-processed using their script.


Where to find in Semeru:
========================


The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/Defect-detection in Semeru


CodeXGLUE -- Defect Detection
=============================


Task Definition
---------------


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.


### Dataset


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.


### Data Format


Three pre-processed .jsonl files, i.e. URL, URL, URL are present


For each file, each line in the uncompressed file represents one function. One row is illustrated below.


* func: the source code
* target: 0 or 1 (vulnerability or not)
* idx: the index of example


### Data Statistics


Data statistics of the dataset are shown in the below table:



Reference
---------



```
@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}
}
```


### Dataset is imported from CodeXGLUE and pre-processed using their script.


Where to find in Semeru:
========================


The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/Defect-detection in Semeru


CodeXGLUE -- Defect Detection
=============================


Task Definition
---------------


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. ### Dataset


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. ### Data Format


Three pre-processed .jsonl files, i.e. URL, URL, URL are present


For each file, each line in the uncompressed file represents one function. One row is illustrated below.


* func: the source code
* target: 0 or 1 (vulnerability or not)
* idx: the index of example ### Data Statistics


Data statistics of the dataset are shown in the below table:



Reference
---------



```
@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}
}
```
TAGS
#license-mit #region-us 
 ### Dataset is imported from CodeXGLUE and pre-processed using their script.


Where to find in Semeru:
========================


The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/Defect-detection in Semeru


CodeXGLUE -- Defect Detection
=============================


Task Definition
---------------


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. ### Dataset


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. ### Data Format


Three pre-processed .jsonl files, i.e. URL, URL, URL are present


For each file, each line in the uncompressed file represents one function. One row is illustrated below.


* func: the source code
* target: 0 or 1 (vulnerability or not)
* idx: the index of example ### Data Statistics


Data statistics of the dataset are shown in the below table:



Reference
---------



```
@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}
}
```
passage: TAGS
#license-mit #region-us 
### Dataset is imported from CodeXGLUE and pre-processed using their script.


Where to find in Semeru:
========================


The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/Defect-detection in Semeru


CodeXGLUE -- Defect Detection
=============================


Task Definition
---------------


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.### Dataset


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.### Data Format


Three pre-processed .jsonl files, i.e. URL, URL, URL are present


For each file, each line in the uncompressed file represents one function. One row is illustrated below.


* func: the source code
* target: 0 or 1 (vulnerability or not)
* idx: the index of example### Data Statistics


Data statistics of the dataset are shown in the below table:



Reference
---------



```
@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}
}
```