Statistical Type Inference for Incomplete Programs

Author:

Peng Yaohui1,Xie Jing1,Yang Qiongling1,Guo Hanwen1,Li Qingan1,Xue Jingling2,Yuan Mengting1

Affiliation:

1. Wuhan University, Wuhan, China

2. University of New South Wales, Sydney, Australia

Funder

National Natural Science Foundation of China

Key Laboratory of Computer System and Architecture

Publisher

ACM

Reference53 articles.

1. Typilus: neural type hints

2. Pavol Bielik , Veselin Raychev , and Martin T . Vechev . 2016 . PHOG : Probabilistic Model for Code. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, Maria-Florina Balcan and Kilian Q. Weinberger (Eds.) (JMLR Workshop and Conference Proceedings , Vol. 48). JMLR.org, 2933– 2942 . http://proceedings.mlr.press/v48/bielik16.html Pavol Bielik, Veselin Raychev, and Martin T. Vechev. 2016. PHOG: Probabilistic Model for Code. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, Maria-Florina Balcan and Kilian Q. Weinberger (Eds.) (JMLR Workshop and Conference Proceedings, Vol. 48). JMLR.org, 2933–2942. http://proceedings.mlr.press/v48/bielik16.html

3. Casper Boone , Niels de Bruin , Arjan Langerak , and Fabian Stelmach . 2019. DLTPy: Deep Learning Type Inference of Python Function Signatures using Natural Language Context. CoRR, abs/1912.00680 ( 2019 ), arxiv:1912.00680. arxiv:1912.00680 Casper Boone, Niels de Bruin, Arjan Langerak, and Fabian Stelmach. 2019. DLTPy: Deep Learning Type Inference of Python Function Signatures using Natural Language Context. CoRR, abs/1912.00680 (2019), arxiv:1912.00680. arxiv:1912.00680

4. Attributed Programmed Graph Grammars and Their Application to Schematic Diagram Interpretation

5. A graph distance metric based on the maximal common subgraph

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