task_categories:text-classification size_categories:10K<n<100K license:cc0-1.0 region:us
---
license: cc0-1.0
task_categories:
- text-classification
pretty_name: Juliet Test Suite 1.3
size_categories:
- 10K<n<100K
---

# Dataset Card for the Juliet Test Suite 1.3

### Dataset Summary

This Datasets contains all test cases from the NIST's [Juliet test suite](https://samate.nist.gov/SARD/test-suites/112) for the C and C++ programming languages. The dataset contains a benign and a defective implementation of each sample, which have been extracting by means of the OMITGOOD and OMITBAD preprocessor macros of the Juliet test suite.

### Supported Tasks and Leaderboards

Software defect prediction, code clone detection.

### Languages

The C and C++ programming languages.

## Dataset Structure

### Data Instances

### Data Fields

| index | name | type | description |
| --- | --- | --- | --- |
| 0 | index | int | The index of each sample in the dataset. |
| 1 | filename | str | The path to the test case including the file name. |
| 2 | class | int | The class of the defect, i.e., the collection by CWE number from which the sample was taken. |
| 3 | good | str | The code of the benign implementation. |
| 4 | bad | str | The code of the defective implementation. |

### Data Splits

| type | size |
|------|------|
| train | 80706 cases |
| test | 20177 cases |

## Dataset Creation

### Curation Rationale

### Source Data

https://samate.nist.gov/SARD/test-suites/112

#### Initial Data Collection and Normalization

#### Who are the source language producers?

### Annotations

#### Annotation process

#### Who are the annotators?

### Personal and Sensitive Information

## Considerations for Using the Data

### Social Impact of Dataset

### Discussion of Biases

### Other Known Limitations

The Juliet test suite is a synthetic dataset, meaning that all samples have been manually crafted. Therefore they are not entirely representative of actual software defects found in the wild. A classifier trained on these samples may suffer from decreased predictive performance, leading to gross misclassifactions. Critical software defects may therefore be overlooked, when such model is applied in a realistic environment.

## Additional Information

### Dataset Curators

https://github.com/lorenz9314/

### Licensing Information

### Citation Information

### Contributions
