  There has been growing interest in automatically predicting missing type
annotations in programs written in Python and JavaScript. While prior methods
have achieved impressive accuracy when predicting the most common types, they
often perform poorly on rare or complex types. In this paper, we present a new
type inference method that treats type prediction as a code infilling task by
leveraging CodeT5, a state-of-the-art seq2seq pre-trained language model for
code. Our method uses static analysis to construct dynamic contexts for each
code element whose type signature is to be predicted by the model. We also
propose an iterative decoding scheme that incorporates previous type
predictions in the model's input context, allowing information exchange between
related code elements. Our evaluation shows that the proposed approach, TypeT5,
not only achieves a higher overall accuracy (particularly on rare and complex
types) but also produces more coherent results with fewer type errors -- while
enabling easy user intervention.

Software Engineering;Machine Learning;Programming Languages