Papers Source Code Summarization

Automatic Source Code Summarization via Reinforcement Learning

Due to the fast development of computer and software, the volume of today's code has reached an unprecedented level. For large-scale systems (e.g., cloud computing systems) with billions lines of codes, the majority of i…

GraphCodeBERT: Pre-training Code Representations with Data Flow

Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarization, etc. However, existing pre-trained mo…

Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations

We propose Corder, a self-supervised contrastive learning framework for source code model. Corder is designed to alleviate the need of labeled data for code retrieval and code summarization tasks. The pre-trained model o…

Contrastive Code Representation Learning

Recent work learns contextual representations of source code by reconstructing tokens from their context. For downstream semantic understanding tasks like summarizing code in English, these representations should ideally…

Retrieval-Augmented Generation for Code Summarization via Hybrid GNN

Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexit…

A Structural Transformer with Relative Positions in Trees for Code-to-Sequence Tasks

We suggest two approaches to incorporate syntactic information into transformer models encoding trees (e.g. abstract syntax trees) and generating sequences. First, we use self-attention with relative position representat…

A Transformer-based Approach for Source Code Summarization

Generating a readable summary that describes the functionality of a program is known as source code summarization. In this task, learning code representation by modeling the pairwise relationship between code tokens to c…

Improved Code Summarization via a Graph Neural Network

Automatic source code summarization is the task of generating natural language descriptions for source code. Automatic code summarization is a rapidly expanding research area, especially as the community has taken greate…

Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning

Code summarization generates brief natural language description given a source code snippet, while code retrieval fetches relevant source code given a natural language query. Since both tasks aim to model the association…

Code Generation as a Dual Task of Code Summarization

Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development. Various neural network-based approaches are proposed to solve these two tasks separately. However, th…

Automatic Source Code Summarization with Extended Tree-LSTM

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 auto…

Recommendations for Datasets for Source Code Summarization

Source Code Summarization is the task of writing short, natural language descriptions of source code. The main use for these descriptions is in software documentation e.g. the one-sentence Java method descriptions in Jav…

A Convolutional Neural Network for Language-Agnostic Source Code Summarization

Descriptive comments play a crucial role in the software engineering process. They decrease development time, enable better bug detection, and facilitate the reuse of previously written code. However, comments are common…

Improving Automatic Source Code Summarization via Deep Reinforcement Learning

Code summarization provides a high level natural language description of the function performed by code, as it can benefit the software maintenance, code categorization and retrieval. To the best of our knowledge, most s…

Structured Neural Summarization

Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on high…

code2seq: Generating Sequences from Structured Representations of Code

The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval. Sequence-to-sequence (seq2seq) models, adopted from neu…

Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow

For tasks like code synthesis from natural language, code retrieval, and code summarization, data-driven models have shown great promise. However, creating these models require parallel data between natural language (NL)…

Summarizing Source Code using a Neural Attention Model