A survey on deep learning for software engineering
In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …
and an improved model training method to break the bottleneck of neural network …
Deep learning for source code modeling and generation: Models, applications, and challenges
Deep Learning (DL) techniques for Natural Language Processing have been evolving
remarkably fast. Recently, the DL advances in language modeling, machine translation, and …
remarkably fast. Recently, the DL advances in language modeling, machine translation, and …
Evaluating large language models trained on code
We introduce Codex, a GPT language model fine-tuned on publicly available code from
GitHub, and study its Python code-writing capabilities. A distinct production version of Codex …
GitHub, and study its Python code-writing capabilities. A distinct production version of Codex …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
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 …
(PL) and natural language (NL), while code translation avails the migration of legacy code …
Graph neural networks for natural language processing: A survey
Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …
Language Processing (NLP). Although text inputs are typically represented as a sequence …
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 …
improvements on a variety of code-related tasks such as code search, code completion …
Open graph benchmark: Datasets for machine learning on graphs
Abstract We present the Open Graph Benchmark (OGB), a diverse set of challenging and
realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine …
realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine …
Cure: Code-aware neural machine translation for automatic program repair
Automatic program repair (APR) is crucial to improve software reliability. Recently, neural
machine translation (NMT) techniques have been used to automatically fix software bugs …
machine translation (NMT) techniques have been used to automatically fix software bugs …
Codebert: A pre-trained model for programming and natural languages
We present CodeBERT, a bimodal pre-trained model for programming language (PL) and
nat-ural language (NL). CodeBERT learns general-purpose representations that support …
nat-ural language (NL). CodeBERT learns general-purpose representations that support …