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arXiv · 1805.08747

EgoCoder: Intelligent Program Synthesis with Hierarchical Sequential Neural Network Model

Jiawei Zhang·Limeng Cui·Fisher B. Gouza
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Abstract

Programming has been an important skill for researchers and practitioners in computer science and other related areas. To learn basic programing skills, a long-time systematic training is usually required for beginners. According to a recent market report, the computer software market is expected to continue expanding at an accelerating speed, but the market supply of qualified software developers can hardly meet such a huge demand. In recent years, the surge of text generation research works provides the opportunities to address such a dilemma through automatic program synthesis. In this paper, we propose to make our try to solve the program synthesis problem from a data mining perspective. To address the problem, a novel generative model, namely EgoCoder, will be introduced in this paper. EgoCoder effectively parses program code into abstract syntax trees (ASTs), where the tree nodes will contain the program code/comment content and the tree structure can capture the program logic flows. Based on a new unit model called Hsu, EgoCoder can effectively capture both the hierarchical and sequential patterns in the program ASTs. Extensive experiments will be done to compare EgoCoder with the state-of-the-art text generation methods, and the experimental results have demonstrated the effectiveness of EgoCoder in addressing the program synthesis problem.

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Jiawei Zhang, Limeng Cui, Fisher B. Gouza. 2018-05-22. EgoCoder: Intelligent Program Synthesis with Hierarchical Sequential Neural Network Model. https://arxiv.org/abs/1805.08747

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