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 …

Context: Recently deep learning based Natural Language Processing (NLP) models have
shown great potential in the modeling of source code. However, a major limitation of these …

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 …

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 …

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 …

Natural Language Processing (NLP) models have been used extensively to study
relationship among words in a corpus. Inspired by models such as n-gram we developed a …

Recent years have witnessed the significant rise of Deep Learning (DL) techniques applied
to source code. Researchers exploit DL for a multitude of tasks and achieve impressive …

Deep learning models have been successfully applied to a variety of software engineering
tasks, such as code classification, summarisation, and bug and vulnerability detection. In …

Attention-based transformer language models have shown significant performance gains in
various natural language tasks. In this work, we explore the impact of transformer language …