Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2022

Abstract

Type inference for dynamic programming languages such as Python is an important yet challenging task. Static type inference techniques can precisely infer variables with enough static constraints but are unable to handle variables with dynamic features. Deep learning (DL) based approaches are feature-agnostic, but they cannot guarantee the correctness of the predicted types. Their performance significantly depends on the quality of the training data (i.e., DL models perform poorly on some common types that rarely appear in the training dataset). It is interesting to note that the static and DL-based approaches offer complementary benefits. Unfortunately, to our knowledge, precise type inference based on both static inference and neural predictions has not been exploited and remains an open challenge. In particular, it is hard to integrate DL models into the framework of rule-based static approaches

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering; Information Systems and Management

Publication

Proceedings of the 44th International Conference on Software Engineering, Pittsburgh, PA, USA, 2022 May 21-29

First Page

2019

Last Page

2030

Identifier

10.1145/3510003.3510038

Publisher

Association for Computing Machinery

City or Country

New York

Additional URL

https://doi.org/10.1145/3510003.3510038

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