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Programming has been an important skill for researchers and practitioners incomputer science and other related areas. To learn basic programing skills, along-time systematic training is usually required for beginners. According to arecent market report, the computer software market is expected to continueexpanding at an accelerating speed, but the market supply of qualified softwaredevelopers can hardly meet such a huge demand. In recent years, the surge oftext generation research works provides the opportunities to address such adilemma through automatic program synthesis. In this paper, we propose to makeour try to solve the program synthesis problem from a data mining perspective.To address the problem, a novel generative model, namely EgoCoder, will beintroduced in this paper. EgoCoder effectively parses program code intoabstract syntax trees (ASTs), where the tree nodes will contain the programcode/comment content and the tree structure can capture the program logicflows. Based on a new unit model called Hsu, EgoCoder can effectively captureboth the hierarchical and sequential patterns in the program ASTs. Extensiveexperiments will be done to compare EgoCoder with the state-of-the-art textgeneration methods, and the experimental results have demonstrated theeffectiveness of EgoCoder in addressing the program synthesis problem.

EgoCoder: Intelligent Program Synthesis with Hierarchical Sequential Neural Network Model