Recent years have seen the successful application of deep learning to software engineering
(SE). In particular, the development and use of pre-trained models of source code has …

Current statistical language modeling techniques, including deep-learning based models,
have proven to be quite effective for source code. We argue here that the special properties …

The usage of deep learning (DL) approaches for software engineering has attracted much
attention, particularly in source code modelling and analysis. However, in order to use DL …

While a large number of pre-trained models of source code have been successfully
developed and applied to a variety of software engineering (SE) tasks in recent years, our …

With the great success of pre-trained models, the pretrain-then-finetune paradigm has been
widely adopted on downstream tasks for source code understanding. However, compared to …

In recent years, deep learning models have shown great potential in source code modeling
and analysis. Generally, deep learning-based approaches are problem-specific and data …

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 …

An increasingly popular set of techniques adopted by software engineering (SE)
researchers to automate development tasks are those rooted in the concept of Deep …

The growing cost of deep learning for source code Page 1 JANUARY 2022 | VOL. 65 | NO. 1 |
COMMUNICATIONS OF THE ACM 31 viewpoints IMA GER YB Y OZZ DE SIGN uniquely …

There has been a recent surge of interest in automating software engineering tasks using
deep learning. This article addresses the problem of code generation, in which the goal is to …