A hybrid code representation learning approach for predicting method names

Program semantic properties such as class names, method names, and variable names and types play an important role in software development and maintenance. Method names are of particular importance because they provide the cornerstone of abstraction for developers to communicate with each other for various purposes (e.g., code review and program comprehension). Existing method name prediction approaches often represent code as lexical tokens or syntactical AST (abstract syntax tree) paths, making them difficult to learn code semantics and hindering their effectiveness in predicting method names. Initial attempts have been made to represent code as execution traces to capture code semantics, but suffer scalability in collecting execution traces. In this paper, we propose a hybrid code representation learning approach, named Meth2Seq, to encode a method as a sequence of distributed vectors. Meth2Seq represents a method as (1) a bag of paths on the program dependence graph, (2) a sequence of typed intermediate representation statements and (3) a sentence of natural language comment, to scalably capture code semantics. The learned sequence of vectors of a method is fed to a decoder model to predict method names. Our evaluation with a dataset of 280.5K methods in 67 Java projects has demonstrated that Meth2Seq outperforms the two state-of-the-art code representation learning approaches in F1-score by 92.6% and 36.6%, while also outperforming two state-of-the-art method name prediction approaches in F1-score by 85.6% and 178.1%. (C) 2021 Elsevier Inc. All rights reserved.