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Deep Code Comment Generation

DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features for better comments generation of Java methods.
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Convolutional Neural Networks over Tree Structures for Programming Language Processing

A novel tree-based convolutional neural network (TBCNN) is proposed for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information.
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Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths

This paper presents SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence, which leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (L STM) units, pick up heterogeneous information along the SDP.
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Summarizing Source Code with Transferred API Knowledge

Experiments on large-scale real-world industry Java projects indicate that the proposed novel approach, named TL-CodeSum, is effective and outperforms the state-of-the-art in code summarization.
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Natural Language Inference by Tree-Based Convolution and Heuristic Matching

This model, a tree-based convolutional neural network (TBCNN) captures sentence-level semantics; then heuristic matching layers like concatenation, element-wise product/difference combine the information in individual sentences.
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How Transferable are Neural Networks in NLP Applications?

In this paper, systematic case studies are conducted and an illuminating picture is provided on the transferability of neural networks in NLP.
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Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation

This paper proposes seq2BF, a “sequence to backward and forward sequences” model, which generates a reply containing the given keyword, and significantly outperforms traditional sequence-to-sequence models in terms of human evaluation and the entropy measure.
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Deep code comment generation with hybrid lexical and syntactical information

Experimental results demonstrate that the method Hybrid-DeepCom outperforms the state-of-the-art by a substantial margin and the results show that reducing the out- of-vocabulary tokens improves the accuracy effectively.
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Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree

The first to apply graph neural networks on the domain of code clone detection and build a graph representation of programs called flow-augmented abstract syntax tree (FA-AST), which outperforms the state-of-the-art approaches on both Google Code Jam and BigCloneBench tasks.
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Code Generation as a Dual Task of Code Summarization

This paper proposes a dual training framework to train the two tasks simultaneously, and considers the dualities on probability and attention weights, and design corresponding regularization terms to constrain the duality.
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