README.md

---
license: mit
dataset_info:
  features:
  - name: idx
    dtype: int64
  - name: project
    dtype: string
  - name: project_url
    dtype: string
  - name: filepath
    dtype: string
  - name: commit_id
    dtype: string
  - name: commit_message
    dtype: string
  - name: is_vulnerable
    dtype: bool
  - name: hash
    dtype: string
  - name: func_name
    dtype: string
  - name: func_body
    dtype: string
  - name: changed_lines
    dtype: string
  - name: changed_statements
    dtype: string
  - name: cve_list
    sequence: string
  - name: cwe_list
    sequence: string
  - name: fixed_func_idx
    dtype: int64
  - name: context
    struct:
    - name: Execution Environment
      sequence: string
    - name: Explanation
      sequence: string
    - name: External Function
      sequence: string
    - name: Function Argument
      sequence: string
    - name: Globals
      sequence: string
    - name: Type Execution Declaration
      sequence: string
  splits:
  - name: train
    num_bytes: 119514243
    num_examples: 25440
  download_size: 30875803
  dataset_size: 119514243
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Dataset Card for Dataset Name

SecVulEval is a collection of real-world C/C++ vulnerabilities.

## Dataset Details

### Dataset Description
The dataset is curated by collecting C/C++ vulnerability from NVD. It features statement-level vulnerable information, context information for vulnerable functions 
(`is_vulnerable=True`), and other metadata such as CVE, CWE, commit information. The dataset contains vulnerable and non-vulnerable function samples.

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### Dataset Sources

The vulnerabilities (CVEs) are collected from NVD (https://nvd.nist.gov). Then, the corresponding patches to the vulnerabilities are collected from their 
respective git repositories.

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## Uses

The dataset comprises both vulnerable (43.23%) and non-vulnerable (56.77%) functions, with a total collection of 25,440 function. This large collection of functions make
it suitable for training vulnerability detection model. The statement-level info, along with contextual information can make context-aware detection at finer-grained
level possible. The dataset can also be used to evaluate C/C++ vulnerability detection models.

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## Dataset Structure

The dataset has 15 different fields.
- The `project_url` column has 735 different values while the `project` column has 707 unique values.
This is because for `project == "Android"`, there are multiple different repositories.
- The `changed_lines` and `changed_statements` columns include the changes in made in the patch as a list of (line, code) pair. 
Vulnerable functions include the deleted lines/statements and the non-vulnerable functions has the added lines/statements.
- Some functions/vulnerabilities can be assigned to more than one CVE/CWE which is why `cve_list` and `cwe_list` are given as lists, although in most cases there would be
only one CVE and CWE id.
- The `fixed_func_id` includes the `idx` number (first field in the dataset) of the corresponding fixed patch of a vulnerable function. This helps to easily pair the 
vulnerable functions with their fixing code. For non-vulnerable code it doesn't make sense for a "fixed" version and the `fixed_func_id` is just itself.
- The `context` field includes contextual information for vulnerable functions according to the five categories as discussed in the paper. It is added as the list of
symbols and an explanation as generated by the LLM.

Other fields are self-explanatory. 

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