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Type inference methods based on deep learning are becoming increasinglypopular as they aim to compensate for the drawbacks of static and dynamicanalysis approaches, such as high uncertainty. However, their practicalapplication is still debatable due to several intrinsic issues such as codefrom different software domains will involve data types that are unknown to thetype inference system. In order to overcome these problems and gainhigh-confidence predictions, we thus present TIPICAL, a method that combinesdeep similarity learning with novelty detection. We show that our method canbetter predict data types in high confidence by successfully filtering outunknown and inaccurate predicted data types and achieving higher F1 scores tothe state-of-the-art type inference method Type4Py. Additionally, weinvestigate how different software domains and data type frequencies may affectthe results of our method.

TIPICAL -- Type Inference for Python In Critical Accuracy Level