MaxSMT-Based Type Inference for Python 3
By Mostafa Hassan, Caterina Urban, Marco Eilers, Peter Müller. 2018
55 citations recorded by the source corpus.
We present Typpete, a sound type inferencer that automatically infers Python 3 type annotations. Typpete encodes type constraints as a MaxSMT problem and uses optional constraints and specific quantifier instantiation patterns to make the constraint solving process efficient. Our experimental evaluation shows that Typpete scales to real world Python programs and outperforms state-of-the-art tools.
Subfields: Artificial Intelligence, Hardware and Architecture, Information Systems, Program Analysis & Verification
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Georgios Gousios
- Amir M. Mir
- Satish Chandra
- Michael Pradel
- Sebastian Proksch
- Evaldas Latoskinas
- Jason Liu
- Milod Kazerounian
In Hardware and Architecture
In Information Systems
- Georgios Gousios
- Satish Chandra
- Jason Liu
- Michael Pradel
- Sebastian Proksch
- Amir M. Mir
- Evaldas Latoskinas
- Charles Sutton
In Program Analysis & Verification
- Georgios Gousios
- Satish Chandra
- Jason Liu
- Amir M. Mir
- Sebastian Proksch
- Evaldas Latoskinas
- Michael Pradel
- Milod Kazerounian
Citations in the corpus
- TypeWriter: neural type prediction with search-based validation (2019, methodology): “Static type inference [4, 8, 14, 17] computes types using, e.”
- Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python (2021, methodology): “Static type inference methods [19, 24] can be employed to support adding these annotations, which is otherwise a manual, cumbersome, and error-prone process [46].”