Datasetpublic

lbk666/juliet_test_suite_c_1_3

Dataset Card for the Juliet Test Suite 1.3 Dataset Summary This Datasets contains all test cases from the NIST's Juliet test suite 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.… See the full description on the dataset page: https://huggingface.co/datasets/lbk666/juliet_test_suite_c_1_3.

sourceHugging Facecc0-1.0updated 3d agoView on Hugging Face

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Dataset Card

Dataset Card for the Juliet Test Suite 1.3

Dataset Summary

This Datasets contains all test cases from the NIST's Juliet test suite 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.