Automatic Source Code Summarization with Extended Tree-LSTM

Abstract

Neural machine translation models are used to automatically generate a document from given source code since this can be regarded as a machine translation task. Source code summarization is one of the components for automatic document generation, which generates a summary in natural language from given source code. This suggests that techniques used in neural machine translation, such as Long Short-Term Memory (LSTM), can be used for source code summarization. However, there is a considerable difference between source code and natural language: Source code is essentially structured, having loops and conditional branching, etc. Therefore, there is some obstacle to apply known machine translation models to source code.Abstract syntax trees (ASTs) capture these structural properties and play an important role in recent machine learning studies on source code. Tree-LSTM is proposed as a generalization of LSTMs for tree-structured data. However, there is a critical issue when applying it to ASTs: It cannot handle a tree that contains nodes having an arbitrary number of children and their order simultaneously, which ASTs generally have such nodes. To address this issue, we propose an extension of Tree-LSTM, which we call Multi-way Tree-LSTM and apply it for source code summarization. As a result of computational experiments, our proposal achieved better results when compared with several state-of-the-art techniques.

A Transformer-based Approach for Source Code Summarization

“…Recent works in code summarization utilize structural information of a program in the form of Abstract Syntax Tree (AST) that can be encoded using tree structure encoders such as Tree-LSTM (Shido et al, 2019), Tree-Transformer (Harer et al, 2019), andGraph Neural Network (LeClair et al, 2020). In contrast, Hu et al (2018a) proposed a structure based traversal (SBT) method to flatten the AST into a sequence and showed improvement over the AST based methods.…”

Learning Sequential and Structural Information for Source Code Summarization

“…Liang and Zhu (2018) proposed a tree-based recursive neural network to represent the syntax tree of code. Shido et al (2019) represented source code using the tree structure encoder of tree-LSTM. Harer et al (2019) adopted tree-transformer to encode the structure of ASTs.…”

“…On the other hand, Shido et al (2019); Harer et al (2019); LeClair et al (2020); Scarselli et al (2008) proposed tree-based models to capture the features of the source code. They used the structural information from parse trees but hardly considered the sequence information of code tokens.…”

Disentangled Code Representation Learning for Multiple Programming Languages

“…We set the threshold as 0.8, which means that the crosslanguage input code pairs are semantic identical if the cosine similarity between them is greater than 0.8. For evaluation, we select LSTM (Sundermeyer et al, 2012), Tree-LSTM (Shido et al, 2019), TBCNN (Mou et al, 2016) and GraphCode-BERT ) models as baselines. Code-to-Code Search During software development process, developers often look for code snippets that offer similar functionality (Kim et al, 2018).…”