Research Article
Software Defect Prediction via Attention-Based Recurrent Neural Network
Table 7
Tuned parameters for DP-ARNN.
| Parameter | Description (value) |
| Embedding_dim | The dimensionality of embedding vectors (30) | Vector_length | The length of each AST vector (2000) | Bi-LSTM units | The number of the Bi-LSTM units per layer (40) | 1st hidden layer nodes | The number of 1st hidden layer nodes (16) | 2nd hidden layer nodes | The number of 2nd hidden layer nodes (24) | Batch_size | The number of training samples that propagated through DP-ARNN at a time (32) | Epoch | One forward/backward pass of all the training samples (20) | Monitor | The evaluation criteria on the validation set (val_acc) | Loss function | The loss function to minimize (binary_crossentropy) | Optimizer | The loss function solver (RMSprop) | Activation | Types of activation used in fully connected layers (tanh, linear, and sigmoid) |
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