Suggesting accurate method and class names

Abstract

Descriptive names are a vital part of readable, and hence maintainable, code. Recent progress on automatically suggesting names for local variables tantalizes with the prospect of replicating that success with method and class names. However, suggesting names for methods and classes is much more difficult. This is because good method and class names need to be functionally descriptive, but suggesting such names requires that the model goes beyond local context. We introduce a neural probabilistic language model for source code that is specifically designed for the method naming problem. Our model learns which names are semantically similar by assigning them to locations, called embeddings, in a high-dimensional continuous space, in such a way that names with similar embeddings tend to be used in similar contexts. These embeddings seem to contain semantic information about tokens, even though they are learned only from statistical co-occurrences of tokens. Furthermore, we introduce a variant of our model that is, to our knowledge, the first that can propose neologisms, names that have not appeared in the training corpus. We obtain state of the art results on the method, class, and even the simpler variable naming tasks. More broadly, the continuous embeddings that are learned by our model have the potential for wide application within software engineering.

MMRUC3: A recommendation approach of move method refactoring using coupling, cohesion, and contextual similarity to enhance software design

“… In this article, both the dependency (coupling and cohesion)‐based and context‐based similarity factors have the same priority in the similarity measurement process of the approach. The future plan is to set priority on the two similarity factors in order to analyze the effectiveness of the two factors separately. As the contextual factor of an application provides useful information, the plan is to utilize this factor in other refactoring techniques, such as extract method, extract class, etc, which are dependent on contexts of components of an application. Investigating the behavior of MMRUC3 by preprocessing the source code through fixing the irrelevant names of code entities using the techniques in the works of Høst and Østvold and Allamanis et al …”

“…• As the contextual factor of an application provides useful information, the plan is to utilize this factor in other refactoring techniques, such as extract method, extract class, etc, which are dependent on contexts of components of an application. • Investigating the behavior of MMRUC3 by preprocessing the source code through fixing the irrelevant names of code entities using the techniques in the works of Høst and Østvold 43 and Allamanis et al 44…”

Using Naming Patterns for Identifying Architectural Technical Debt

“…The approach of this proposal gives relevance to class names, and these determines the maintainability and legibility of software, between others [4,7,18,28,29,33,34]. Looking at the standard ISO/IEC FDIS 25010, maintainability includes the following quality attributes: modularity, reusability, analyzability, modifiability and testability.…”

A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques

“…Although most of the aforementioned approaches adopt encoder and decoder structure, Allamanis et al [16] adopt a log-bilinear model in neural network to suggest method and class names. Their model leverages continuous embedding to represent identifier name.…”

“…At the same time, researchers propose other approaches to improving the readability of programs too. For example, some researchers have tried to define identifiers with a long descriptive name in order to implement self-commented code [15,16]. However, it makes code comprehension more difficult [17,18].…”