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DLTPy: Deep Learning Type Inference of Python Function Signatures using Natural Language Context
Casper Boone, Niels de Bruin, Arjan Langerak, Fabian Stelmach
Due to the rise of machine learning, Python is an increasingly popular programming language. Python, however, is dynamically typed. Dynamic typing has shown to have drawbacks when a project grows, while at the same time it improves developer productivity. To have the benefits of static typing, combined with high developer productivity, types need to be inferred. In this paper, we present DLTPy: a deep learning type inference solution for the prediction of types in function signatures based on the natural language context (identifier names, comments and return expressions) of a function. We found that DLTPy is effective and has a top-3 F1-score of 91.6%. This means that in most of the cases the correct type is within the top-3 predictions. We conclude that natural language contained in comments and return expressions are beneficial to predicting types more accurately. DLTPy does not significantly outperform or underperform the previous work NL2Type for Javascript, but does show that similar prediction is possible for Python.
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