DeeperCoder: Code Generation Using Machine Learning
Publication Date
1-1-2020
Document Type
Conference Proceeding
Publication Title
2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020
DOI
10.1109/CCWC47524.2020.9031149
First Page
194
Last Page
199
Abstract
In this paper, we present a program generation system based on input and output specification. The system is developed based on a programming-by-example technique used in program synthesis. The system can generate computer programs that satisfies user requirements based on inputs and outputs. We created a simple Domain Specific Language (DSL) which will be used in program synthesis. We trained our neural network with a large set of input space and store corresponding sample training programs. To get the final output which satisfies all the user specifications, we used inductive program synthesis and machine learning. We also experimented with different deep learning models to obtain the desired results with reduced number of steps and execution time. Finally, we show three layers of neural networks with LeakyReLU achieves the best performance when compared to other approaches.
Keywords
Deep Learning, DeepCoder, Domain Specific Language, Neural Networks, Program Synthesis, Programming Languages
Department
Applied Data Science
Recommended Citation
Simon Shim, Pradnyesh Patil, Rajiv Ramesh Yadav, Anurag Shinde, and Venkatesh Devale. "DeeperCoder: Code Generation Using Machine Learning" 2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020 (2020): 194-199. https://doi.org/10.1109/CCWC47524.2020.9031149
