This paper provides a comprehensive review of the literature concerning the utilization of
Natural Language Processing (NLP) techniques, with a particular focus on transformer …

Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …

Pre-trained models for programming languages have recently demonstrated great success
on code intelligence. To support both code-related understanding and generation tasks …

Recent years have seen the successful application of large pre-trained models to code
representation learning, resulting in substantial improvements on many code-related …

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 …

Code representation learning, which aims to encode the semantics of source code into
distributed vectors, plays an important role in recent deep-learning-based models for code …

Code review is an essential activity for ensuring the quality and maintainability of software
projects. However, it is a time-consuming and often error-prone task that can significantly …

Backdoor attacks for neural code models have gained considerable attention due to the
advancement of code intelligence. However, most existing works insert triggers into task …

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 …

Software testing assures that code changes do not adversely affect existing functionality.
However, a test case can be flaky, ie, passing and failing across executions, even for the …