Efficiently representing source code is crucial for various software engineering tasks such as code classification and clone detection. Existing approaches primarily use Abstract Syntax Tree (AST), and only a few focus on semantic graphs such as Control Flow Graph (CFG) and Program Dependency Graph (PDG), which contain information about source code that AST does not. Even though some works tried to utilize multiple representations, they do not provide any insights about the costs and benefits of using multiple representations. The primary goal of this paper is to discuss the implications of utilizing multiple code representations, specifically AST, CFG, and PDG. We modify an AST path-based approach to accept multiple representations as input to an attention-based model. We do this to measure the impact of additional representations (such as CFG and PDG) over AST. We evaluate our approach on three tasks: Method Naming, Program Classification, and Clone Detection. Our approach increases the performance on these tasks by 11% (F1), 15.7% (Accuracy), and 9.3% (F1), respectively, over the baseline. In addition to the effect on performance, we discuss timing overheads incurred with multiple representations. We envision this work providing researchers with a lens to evaluate combinations of code representations for various tasks.

翻译：高效表示源代码对于代码分类和克隆检测等各类软件工程任务至关重要。现有方法主要采用抽象语法树（AST），仅有少数研究关注包含AST所不具备的源代码信息的语义图，例如控制流图（CFG）和程序依赖图（PDG）。尽管已有工作尝试利用多种表示方式，但它们并未阐明使用多种表示所需的成本与收益。本文的主要目标是探讨使用多种代码表示（特别是AST、CFG和PDG）的意义。我们改进了一种基于AST路径的方法，使其能够将多种表示作为注意力模型的输入，从而评估在AST基础上增加额外表示（如CFG和PDG）的影响。我们在三个任务上评估了该方法：方法命名、程序分类和克隆检测。与基线相比，我们的方法分别将这三项任务的性能提升了11%（F1值）、15.7%（准确率）和9.3%（F1值）。除性能影响外，我们还讨论了多种表示带来的时间开销。我们希望这项工作能为研究人员评估不同代码表示组合在各种任务中的适用性提供参考。