Codebert Base Finetuned Detect Insecure Code

🚀 CodeBERT fine-tuned for Insecure Code Detection 💾⛔

This model is a fine-tuned version of codebert-base on the CodeXGLUE -- Defect Detection dataset for the Insecure Code Detection downstream task.

✨ Features

Details of CodeBERT

We present CodeBERT, a bimodal pre-trained model for programming language (PL) and natural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language code search, code documentation generation, etc.

We develop CodeBERT with a Transformer-based neural architecture and train it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators. This enables us to utilize both bimodal data of NL-PL pairs and unimodal data, where the former provides input tokens for model training while the latter helps to learn better generators.

We evaluate CodeBERT on two NL-PL applications by fine-tuning model parameters. Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks. Furthermore, to investigate what type of knowledge is learned in CodeBERT, we construct a dataset for NL-PL probing and evaluate in a zero-shot setting where parameters of pre-trained models are fixed. Results show that CodeBERT performs better than previous pre-trained models on NL-PL probing.

Details of the downstream task (code classification) - Dataset 📚

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 attacks. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.

The dataset used comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. All projects are combined and split 80%/10%/10% for training/dev/test.

The data statistics of the dataset are shown in the following table:

Test set metrics 🧾

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
tokenizer = AutoTokenizer.from_pretrained('mrm8488/codebert-base-finetuned-detect-insecure-code')
model = AutoModelForSequenceClassification.from_pretrained('mrm8488/codebert-base-finetuned-detect-insecure-code')

inputs = tokenizer("your code here", return_tensors="pt", truncation=True, padding='max_length')
labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
outputs = model(**inputs, labels=labels)
loss = outputs.loss
logits = outputs.logits

print(np.argmax(logits.detach().numpy()))

Created by Manuel Romero/@mrm8488 | LinkedIn

Made with ♥ in Spain