DeepJIT: An End-To-End Deep LearningFramework for Just-In-Time Defect Prediction [pdf]

Contact

Questions and discussion are welcome: vdthoang.2016@smu.edu.sg

Implementation Environment

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

Data & Pretrained models:

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

After downloading, simply copy the data and model folders to DeepJIT folder.

Hyperparameters:

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.

Running and evalutation

• 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]

Contact

Questions and discussion are welcome: vdthoang.2016@smu.edu.sg