| --- |
| language: en |
| datasets: |
| - codexglue |
| --- |
| |
| # CodeBERT fine-tuned for Insecure Code Detection 💾⛔ |
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| [codebert-base](https://huggingface.co/microsoft/codebert-base) fine-tuned on [CodeXGLUE -- Defect Detection](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection) dataset for **Insecure Code Detection** downstream task. |
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| ## Details of [CodeBERT](https://arxiv.org/abs/2002.08155) |
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| We present CodeBERT, a bimodal pre-trained model for programming language (PL) and nat-ural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language codesearch, code documentation generation, etc. We develop CodeBERT with 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. |
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| ## Details of the downstream task (code classification) - Dataset 📚 |
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| 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. |
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| The [dataset](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection) used comes from the paper [*Devign*: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](http://papers.nips.cc/paper/9209-devign-effective-vulnerability-identification-by-learning-comprehensive-program-semantics-via-graph-neural-networks.pdf). All projects are combined and splitted 80%/10%/10% for training/dev/test. |
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| Data statistics of the dataset are shown in the below table: |
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| | | #Examples | |
| | ----- | :-------: | |
| | Train | 21,854 | |
| | Dev | 2,732 | |
| | Test | 2,732 | |
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| ## Test set metrics 🧾 |
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| | Methods | ACC | |
| | -------- | :-------: | |
| | BiLSTM | 59.37 | |
| | TextCNN | 60.69 | |
| | [RoBERTa](https://arxiv.org/pdf/1907.11692.pdf) | 61.05 | |
| | [CodeBERT](https://arxiv.org/pdf/2002.08155.pdf) | 62.08 | |
| | [Ours](https://huggingface.co/mrm8488/codebert-base-finetuned-detect-insecure-code) | **65.30** | |
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| ## Model in Action 🚀 |
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| ```python |
| 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') |
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| 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 |
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| print(np.argmax(logits.detach().numpy())) |
| ``` |
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| > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) |
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| > Made with <span style="color: #e25555;">♥</span> in Spain |
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