Inferring Javascript types using Graph Neural Networks.

作者: Jessica Schrouff , Kai Wohlfahrt , Liam Atkinson , Bruno Marnette

DOI:

关键词: Code (cryptography)Computer scienceArtificial intelligenceJavaScriptDeep learningGraph neural networksSecurity tokenSource codeProgramming language

摘要: … As a first step towards automatic code repair, we implemented a graph neural network model that predicts token types for Javascript programs. The predictions achieve an accuracy …

参考文章(15)
Zoubin Ghahramani, Yarin Gal, Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning arXiv: Machine Learning. ,(2015)
F. Scarselli, M. Gori, Ah Chung Tsoi, M. Hagenbuchner, G. Monfardini, The Graph Neural Network Model IEEE Transactions on Neural Networks. ,vol. 20, pp. 61- 80 ,(2009) , 10.1109/TNN.2008.2005605
Veselin Raychev, Martin Vechev, Andreas Krause, Predicting Program Properties from "Big Code" symposium on principles of programming languages. ,vol. 50, pp. 111- 124 ,(2015) , 10.1145/2676726.2677009
Max Welling, Thomas N. Kipf, Semi-Supervised Classification with Graph Convolutional Networks arXiv: Learning. ,(2016)
Zhaogui Xu, Xiangyu Zhang, Lin Chen, Kexin Pei, Baowen Xu, Python probabilistic type inference with natural language support foundations of software engineering. pp. 607- 618 ,(2016) , 10.1145/2950290.2950343
Patrick F. Riley, Justin Gilmer, George E. Dahl, Oriol Vinyals, Samuel S. Schoenholz, Neural Message Passing for Quantum Chemistry international conference on machine learning. pp. 1263- 1272 ,(2017)
M. Welling, T.N. Kipf, R. van den Berg, Graph Convolutional Matrix Completion arXiv: Machine Learning. ,(2017)
Benjamin Recht, Ameet Talwalkar, Ekaterina Gonina, Kevin G. Jamieson, Moritz Hardt, Afshin Rostamizadeh, Liam Li, Massively Parallel Hyperparameter Tuning ,(2018)
Richard Liaw, Robert Nishihara, Philipp Moritz, Ion Stoica, Eric Liang, Joseph E. Gonzalez, Tune: A Research Platform for Distributed Model Selection and Training. arXiv: Learning. ,(2018)