Static Type Recommendation for Python

Abstract

Recently, Python has adopted optional type annotation to support type checking and program documentation. However, to enjoy the benefits, developers have to manually write type annotations, which is recognized to be a time-consuming task. To alleviate human efforts on manual type annotation, machine-learning-based approaches have been proposed to recommend types based on code features. However, they suffer from the correctness problem, i.e., the recommended types cannot pass type checking. To address the correctness problem of the machine-learning-based approaches, in this paper, we present a static type recommendation approach, named Stray. Stray can recommend types correctly. We evaluate Stray by comparing it against four state-of-art type recommendation approaches, and find that Stray outperforms these baselines by over 30% absolute improvement in both precision and recall.