---
dataset_info:
  features:
  - name: input
    dtype: string
  - name: output
    dtype: string
  splits:
  - name: validation
    num_bytes: 15586336
    num_examples: 15809
  - name: train
    num_bytes: 125099945
    num_examples: 126477
  - name: test
    num_bytes: 15640963
    num_examples: 15810
  download_size: 33528231
  dataset_size: 156327244
---
# Dataset Card for "AGabs_finetuning"

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. 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	126,477
Dev	15,809
Test	15,810