The application of deep learning techniques in software engineering becomes increasingly popular. One key problem is developing high-quality and easy-to-use source code representations for code-related tasks. The research community has acquired impressive results in recent years. However, due to the deployment difficulties and performance bottlenecks, seldom these approaches are applied to the industry. In this paper, we present xASTNN, an eXtreme Abstract Syntax Tree (AST)-based Neural Network for source code representation, aiming to push this technique to industrial practice. The proposed xASTNN has three advantages. First, xASTNN is completely based on widely-used ASTs and does not require complicated data pre-processing, making it applicable to various programming languages and practical scenarios. Second, three closely-related designs are proposed to guarantee the effectiveness of xASTNN, including statement subtree sequence for code naturalness, gated recursive unit for syntactical information, and gated recurrent unit for sequential information. Third, a dynamic batching algorithm is introduced to significantly reduce the time complexity of xASTNN. Two code comprehension downstream tasks, code classification and code clone detection, are adopted for evaluation. The results demonstrate that our xASTNN can improve the state-of-the-art while being faster than the baselines.

翻译：深度学习技术在软件工程中的应用日益普及，其中一个关键问题是为代码相关任务开发高质量且易于使用的源代码表示。近年来，学术界已取得显著成果。然而，由于部署困难和性能瓶颈，这些方法很少被应用于工业领域。本文提出xASTNN，一种基于极简抽象语法树的神经网络源代码表示方法，旨在将该技术推向工业实践。所提出的xASTNN具有三大优势。第一，xASTNN完全基于广泛使用的抽象语法树，无需复杂的数据预处理，使其适用于多种编程语言和实际场景。第二，本文提出三项紧密关联的设计以确保xASTNN的有效性，包括用于代码自然性的语句子树序列、用于语法信息的门控递归单元和用于序列信息的门控循环单元。第三，引入动态批处理算法以显著降低xASTNN的时间复杂度。采用代码分类和代码克隆检测两个代码理解下游任务进行评估，结果表明，xASTNN在提升最先进方法性能的同时，运行速度也优于基线方法。