Convolutional Neural Networks over Tree Structures for Programming Language Processing
Abstract
Programming language processing (similar to natural language processing) is a hot research topic in the field of software engineering; it has also aroused growing interest in the artificial intelligence community. However, different from a natural language sentence, a program contains rich, explicit, and complicated structural information. Hence, traditional NLP models may be inappropriate for programs. In this paper, we propose a novel tree-based convolutional neural network (TBCNN) for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information. TBCNN is a generic architecture for programming language processing; our experiments show its effectiveness in two different program analysis tasks: classifying programs according to functionality, and detecting code snippets of certain patterns. TBCNN outperforms baseline methods, including several neural models for NLP.
Cite
@article{arxiv.1409.5718,
title = {Convolutional Neural Networks over Tree Structures for Programming Language Processing},
author = {Lili Mou and Ge Li and Lu Zhang and Tao Wang and Zhi Jin},
journal= {arXiv preprint arXiv:1409.5718},
year = {2015}
}
Comments
Accepted at AAAI-16