TIPICAL -- Type Inference for Python In Critical Accuracy Level

TL;DR

TIPICAL is a novel deep learning-based type inference method for Python that improves high-confidence predictions by filtering out unknown types, outperforming existing methods like Type4Py.

Contribution

It introduces a combined approach of deep similarity learning and novelty detection to enhance type inference accuracy and confidence in Python code analysis.

Findings

TIPICAL achieves higher F1 scores than Type4Py.

The method effectively filters out unknown and inaccurate data types.

Software domain and data type frequency influence inference results.

Abstract

Type inference methods based on deep learning are becoming increasingly popular as they aim to compensate for the drawbacks of static and dynamic analysis approaches, such as high uncertainty. However, their practical application is still debatable due to several intrinsic issues such as code from different software domains will involve data types that are unknown to the type inference system. In order to overcome these problems and gain high-confidence predictions, we thus present TIPICAL, a method that combines deep similarity learning with novelty detection. We show that our method can better predict data types in high confidence by successfully filtering out unknown and inaccurate predicted data types and achieving higher F1 scores to the state-of-the-art type inference method Type4Py. Additionally, we investigate how different software domains and data type frequencies may affect the results of our method.