Dynamic programming languages (DPLs), such as Python and Ruby, are often used for their flexibility and fast development. The absence of static typing can lead to runtime exceptions and reduced program understandability. To overcome these problems, some DPLs have introduced optional static typing. Because of the tedious effort of adding type annotations to existing projects, different approaches have been employed to generate type annotations. Static type inference methods are sound in their suggestions, but the dynamic nature of DPLs, combined with insufficient satisfied static dependencies, can cause imprecision. Other proposed approaches used machine learning (ML)-based type inference to predict type annotations. ML-based methods don’t have the limitations of static type inference, however, their performance depends on the training set’s quality and they cannot guarantee type correctness because of their probabilistic techniques. One of such ML-based inference approaches, is the state-of-the-art Type4Py model. Type4Py suffers from some of the same limitations of other learning-based approaches, e.g. it cannot predict types outside of its pre-defined type clusters. To this end, this paper presents hpredict, a tool that combines type prediction of Type4Py’s pre-trained model with static type inference. hpredict runs Type4Py’s learning-based inference and static type inference on different copies of type slots and combines the predictions from both methods. Experiments on the test set of the ManyTypes4Py dataset show that hpredict outperforms Type4Py significantly by 11% regarding Top-10 prediction. The findings of this research, lend evidence that hpredict can increase Type4Py’s general type prediction performance by employing static type inference as well.

Researchers at the Delft University of Technology have developed Type4Py: a tool that uses Machine Learning to predict types for Python code. These predictions can be applied by developers to their python code to increase readability and can later be tested by a type-checker for possible type-errors. If a prediction does not return a type-error that prediction is called type-correct. Type4Py has been evaluated by matching its predictions with earlier annotations, also called ground-truth, and has gotten an MRR of 71.7%. However, Type4Py’s predictions have not been evaluated on their typecorrectness. Therefore, I sought out to answer the following research question: How well does Type4Py perform when validated by the static typechecker Mypy? I answered this research question by answering two sub-questions: How many of Type4Py’s predictions are type-correct? And how many of Type4Py’s predictions are type-correct and match ground-truth? I tested a cleaned subset of the ManyTypes4Py dataset with Mypy by running a greedy strategy where I would always pick Type4Py’s prediction with the highest confidence on three different confidence thresholds: 0.25, 0.5 and 0.75 and reached accuracies in terms of typecorrectness of 88%, 91% and 95% for those, respectively. For the case where Type4Py’s predictions matched ground-truth, the predictions on those same thresholds reached accuracies in terms of type-correctness of 95%, 97% and 98%. Comparing this with a similar Type predictor namely, Typilus . Type4Py’s predictions are more typecorrect with a confidence level of at most 50%.