Learning from Code Repositories to Recommend Model Classes
Learning from Code Repositories to Recommend Model Classes
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从代码库学习推荐模型类
DOI：
10.5381/jot.2022.21.3.a4
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
B. Vanderose
中科院分区：
文献类型：
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作者：
Thibaut Capuano;H. Sahraoui;Benoît Frénay;B. Vanderose
随着机器学习算法的日益普及，代码完成，特别是方法调用完成，已经取得了巨大的进步。这些进步也是可能的，这要归功于大型代码存储库的可用性，可以从方法调用完成问题的明确定义的边界中进行学习。然而，设计完成的情况并非如此，模型存储库稀缺，并且设计完成的可能性空间理论上是无限的。我们在本文中提出了一种从代码存储库中学习领域概念及其关系的数字表示的方法，以便为 UML 类图推荐类
With the growing popularity of machine learning algorithms, dramatic advances have been made for code completion, and specifically method-call completion. These advances were also possible thanks to the availability of large code repositories to learn from and to the well-defined boundaries of the method-call completion problem. This is, however, not the case for design completion, where model repositories are scarce and the space of possibilities for design completion is theoretically infinite. We propose in this paper an approach that learns numeric representations of domain concepts and their relations from code repositories in order to recommend classes for UML class diagrams