code2seq: Generating Sequences from Structured Representations of Code

ICLR 2019 • tech-srl/code2seq •

The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval.

CODE SUMMARIZATION

Summarizing Source Code using a Neural Attention Model

ACL 2016 • sriniiyer/codenn •

CODE SUMMARIZATION

A Transformer-based Approach for Source Code Summarization

ACL 2020 • wasiahmad/NeuralCodeSum •

Generating a readable summary that describes the functionality of a program is known as source code summarization.

CODE SUMMARIZATION

Contrastive Code Representation Learning

9 Jul 2020 • parasj/contracode •

We propose Contrastive Code Representation Learning (ContraCode), a self-supervised algorithm for learning task-agnostic semantic representations of programs via contrastive learning.

METHOD NAME PREDICTION REPRESENTATION LEARNING TYPE PREDICTION

Structured Neural Summarization

ICLR 2019 • CoderPat/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.

CODE SUMMARIZATION

Improved Code Summarization via a Graph Neural Network

6 Apr 2020 • acleclair/ICPC2020_GNN •

The first approaches to use structural information flattened the AST into a sequence.

CODE SUMMARIZATION

Automatic Source Code Summarization with Extended Tree-LSTM

19 Jun 2019 • sh1doy/summarization_tf •

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.

CODE SUMMARIZATION MACHINE TRANSLATION

Code Generation as a Dual Task of Code Summarization

NeurIPS 2019 • code-gen/cgcs •

Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development.

CODE GENERATION CODE SUMMARIZATION

Improving Automatic Source Code Summarization via Deep Reinforcement Learning

17 Nov 2018 • mf1832146/tree_transformer_2.0 •

To the best of our knowledge, most state-of-the-art approaches follow an encoder-decoder framework which encodes the code into a hidden space and then decode it into natural language space, suffering from two major drawbacks: a) Their encoders only consider the sequential content of code, ignoring the tree structure which is also critical for the task of code summarization, b) Their decoders are typically trained to predict the next word by maximizing the likelihood of next ground-truth word with previous ground-truth word given.

CODE SUMMARIZATION