Papers Source Code Summarization

Retrieval-based Layer-wise Adaptive Transformer for Source Code Summarization

We propose a model that learns both the sequential and the structural features of code for source code summarization. We adopt the Abstract Syntax Tree (AST) and graph convolution to model the structural information and …

Meta Learning for Code Summarization

Source code summarization is the task of generating a high-level natural language description for a segment of programming language code. Current neural models for the task differ in their architecture and the aspects of…

Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization

Automatic code summarization, which aims to describe the source code in natural language, has become an essential task in software maintenance. Our fellow researchers have attempted to achieve such a purpose through vari…

GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search

Code search aims to retrieve accurate code snippets based on a natural language query to improve software productivity and quality. With the massive amount of available programs such as (on GitHub or Stack Overflow), ide…

Learning Sequential and Structural Information for Source Code Summarization

On the Evaluation of Neural Code Summarization

Source code summaries are important for program comprehension and maintenance. However, there are plenty of programs with missing, outdated, or mismatched summaries. Recently, deep learning techniques have been exploited…

Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors

Automated source code summarization is a popular software engineering research topic wherein machine translation models are employed to "translate" code snippets into relevant natural language descriptions. Most evaluati…

CoDesc: A Large Code-Description Parallel Dataset

Translation between natural language and source code can help software development by enabling developers to comprehend, ideate, search, and write computer programs in natural language. Despite growing interest from the …

Code Structure Guided Transformer for Source Code Summarization

Code summaries help developers comprehend programs and reduce their time to infer the program functionalities during software maintenance. Recent efforts resort to deep learning techniques such as sequence-to-sequence mo…

CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing

Currently, a growing number of mature natural language processing applications make people's life more convenient. Such applications are built by source code - the language in software engineering. However, the applicati…

HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks

Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells. In this paper, we propose a new task of code documentation generation (CDG) for computational notebooks. In…

Project-Level Encoding for Neural Source Code Summarization of Subroutines

Source code summarization of a subroutine is the task of writing a short, natural language description of that subroutine. The description usually serves in documentation aimed at programmers, where even brief phrase (e.…

Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach

Code summaries are brief natural language descriptions of source code pieces. The main purpose of code summarization is to assist developers in understanding code and to reduce documentation workload. In this paper, we d…

Language-Agnostic Representation Learning of Source Code from Structure and Context

Source code (Context) and its parsed abstract syntax tree (AST; Structure) are two complementary representations of the same computer program. Traditionally, designers of machine learning models have relied predominantly…

Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting

Automatic code summarization frees software developers from the heavy burden of manual commenting and benefits software development and maintenance. Abstract Syntax Tree (AST), which depicts the source code's syntactic s…

Unified Pre-training for Program Understanding and Generation

Code summarization and generation empower conversion between programming language (PL) and natural language (NL), while code translation avails the migration of legacy code from one PL to another. This paper introduces P…

Neural Code Summarization

Code summarization is the task of generating readable summaries that are semantically meaningful and can accurately describe the presumed task of a software. Program comprehension has become one of the most tedious tasks…

WheaCha: A Method for Explaining the Predictions of Models of Code

Attribution methods have emerged as a popular approach to interpreting model predictions based on the relevance of input features. Although the feature importance ranking can provide insights of how models arrive at a pr…

GN-Transformer: Fusing AST and Source Code information in Graph Networks

As opposed to natural languages, source code understanding is influenced by grammar relations between tokens regardless of their identifier name. Considering graph representation of source code such as Abstract Syntax Tr…

Code Summarization with Structure-induced Transformer

Code summarization (CS) is becoming a promising area in recent language understanding, which aims to generate sensible human language automatically for programming language in the format of source code, serving in the mo…