论文标题
更好地通过代码概念图表的多模式学习来更好地建模编程世界
Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning
论文作者
论文摘要
近年来，由于基于最先进的模型体系结构的自然语言处理方法的设计，代码建模的进展一直是巨大的。尽管如此，我们认为当前的最新目的还没有足够关注数据可能带给软件工程学习过程的全部潜力。我们的愿景阐明了利用多模式学习方法来建模编程世界的想法。在本文中，我们调查了我们的愿景的基本思想之一，其基于标识符概念图的目标旨在利用通过特定语言构造操纵的领域概念之间的高级关系。特别是，我们建议通过基于我们的概念图的图形神经网络联合学习来增强现有的代码语言模型。我们进行了初步评估，该评估显示了使用简单的联合学习方法的模型对代码搜索的有效性提高，并提示我们进一步研究我们的研究愿景。
The progress made in code modeling has been tremendous in recent years thanks to the design of natural language processing learning approaches based on state-of-the-art model architectures. Nevertheless, we believe that the current state-of-the-art does not focus enough on the full potential that data may bring to a learning process in software engineering. Our vision articulates on the idea of leveraging multi-modal learning approaches to modeling the programming world. In this paper, we investigate one of the underlying idea of our vision whose objective based on concept graphs of identifiers aims at leveraging high-level relationships between domain concepts manipulated through particular language constructs. In particular, we propose to enhance an existing pretrained language model of code by joint-learning it with a graph neural network based on our concept graphs. We conducted a preliminary evaluation that shows gain of effectiveness of the models for code search using a simple joint-learning method and prompts us to further investigate our research vision.
