Convolutional Neural Networks over Tree Structures for Programming Language Processing

Understand

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.