DeepJIT: An End-To-End Deep LearningFramework for Just-In-Time Defect Prediction [pdf]
Questions and discussion are welcome: [email protected]
Please install the neccessary libraries before running our tool:
- python==3.6.9
- torch==1.2.0
- tqdm==4.46.1
- nltk==3.4.5
- numpy==1.16.5
- scikit-learn==0.22.1
Please following the link below to download the data and pretrained models of our paper.
- https://drive.google.com/drive/folders/1sMl2-LbVi3__56kMeS8YSUW7qaKrL2sQ?usp=sharing
- https://zenodo.org/record/3965246#.XyEDVnUzY5k
After downloading, simply copy the data and model folders to DeepJIT folder.
We have a number of different parameters
- --embedding_dim: Dimension of embedding vectors.
- --filter_sizes: Sizes of filters used by the convolutional neural network.
- --num_filters: Number of filters.
- --hidden_layers: Number of hidden layers.
- --dropout_keep_prob: Dropout for training.
- --l2_reg_lambda: Regularization rate.
- --learning_rate: Learning rate.
- --batch_size: Batch size.
- --num_epochs: Number of epochs.
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To train the model for just-in-time defect prediction, please follow this command:
$ python main.py -train -train_data [path of our data] -dictionary_data [path of our dictionary data] -
To evaluate the model for just-in-time defect prediction, please follow this command:
$ python main.py -predict -pred_data [path of our data] -dictionary_data [path of our dictionary data] -load_model [path of our model]
Questions and discussion are welcome: [email protected]