  Neural Code Intelligence -- leveraging deep learning to understand, generate,
and optimize code -- holds immense potential for transformative impacts on the
whole society. Bridging the gap between Natural Language and Programming
Language, this domain has drawn significant attention from researchers in both
research communities over the past few years. This survey presents a systematic
and chronological review of the advancements in code intelligence, encompassing
over 50 representative models and their variants, more than 20 categories of
tasks, and an extensive coverage of over 680 related works. We follow the
historical progression to trace the paradigm shifts across different research
phases (e.g., from modeling code with recurrent neural networks to the era of
Large Language Models). Concurrently, we highlight the major technical
transitions in models, tasks, and evaluations spanning through different
stages. For applications, we also observe a co-evolving shift. It spans from
initial endeavors to tackling specific scenarios, through exploring a diverse
array of tasks during its rapid expansion, to currently focusing on tackling
increasingly complex and varied real-world challenges. Building on our
examination of the developmental trajectories, we further investigate the
emerging synergies between code intelligence and broader machine intelligence,
uncovering new cross-domain opportunities and illustrating the substantial
influence of code intelligence across various domains. Finally, we delve into
both the opportunities and challenges associated with this field, alongside
elucidating our insights on the most promising research directions. An ongoing,
dynamically updated project and resources associated with this survey have been
released at https://github.com/QiushiSun/NCISurvey.
