Implementation

To extract type hints, we first extract Abstract Syntax Trees (ASTs) and perform light-weight static analysis using our LibSA4Py package. NLP tasks are applied using NLTK. To train the Word2Vec model, we use the gensim package.

For the Type4Py model, we use bidirectional LSTMs in PyTorch to implement the two RNNs. To avoid overfitting, we apply the Dropout regularization to the input sequences. To minimize the value of the Triplet loss function, we employ the Adam optimizer. Also, to speed up the training process, we use the data parallelism feature of PyTorch. For fast KNN search, we use Annoy