  As the complexity of modern software continues to escalate, software
engineering has become an increasingly daunting and error-prone endeavor. In
recent years, the field of Neural Code Intelligence (NCI) has emerged as a
promising solution, leveraging the power of deep learning techniques to tackle
analytical tasks on source code with the goal of improving programming
efficiency and minimizing human errors within the software industry. Pretrained
language models have become a dominant force in NCI research, consistently
delivering state-of-the-art results across a wide range of tasks, including
code summarization, generation, and translation. In this paper, we present a
comprehensive survey of the NCI domain, including a thorough review of
pretraining techniques, tasks, datasets, and model architectures. We hope this
paper will serve as a bridge between the natural language and programming
language communities, offering insights for future research in this rapidly
evolving field.
