Transformer-based models have demonstrated state-of-the-art performance in many
intelligent coding tasks such as code comment generation and code completion. Previous …

In recent years, there has been a wide interest in designing deep neural network-based
models that automate downstream software engineering tasks on source code, such as …

Adapting deep learning (DL) techniques to automate nontrivial coding activities, such as
code documentation and defect detection, has been intensively studied recently. Learning to …

Many Transformer-based pre-trained models for code have been developed and applied to
code-related tasks. In this paper, we review the existing literature, examine the suitability of …

Deep learning (DL) techniques are gaining more and more attention in the software
engineering community. They have been used to support several code-related tasks, such …

Pretrained models for code have exhibited promising performance across various code-
related tasks, such as code summarization, code completion, code translation, and bug …

Deep learning (DL) techniques have been used to support several code-related tasks such
as code summarization and bug-fixing. In particular, pre-trained transformer models are on …

In software engineering (SE), code classification and related tasks, such as code clone
detection are still challenging problems. Due to the elusive syntax and complicated …

Code models have made significant advancements in code intelligence by encoding
knowledge about programming languages. While previous studies have explored the …

Transformers have revolutionized natural language processing (NLP) and have had a huge
impact on automating tasks. Recently, transformers have led to the development of powerful …