Class Name Recommendation Based on Graph Embedding of Program Elements

Abstract

In software development, the quality of identifier names is important because it greatly affects program comprehension for developers. However, naming identifiers that appropriately represent the nature or behavior of program elements such as classes and methods is a difficult task requiring rich development experience and software domain knowledge. Although several studies proposed techniques for recommending identifier names, there are few studies targeting class names and they have limited availability. This paper proposes a novel class name recommendation approach widely available in software development. The key idea is to represent quantitatively the nature or behavior of classes by leveraging embedding technology for heterogeneous graphs. This makes it possible to recommend class names even where a previous approach cannot work. Experimental results suggest that the proposed approach can produce more accurate class name recommendation regardless of whether classes are used. In addition, a further experiment reveals a situation where the proposed approach is particularly effective.