The field of code intelligence is experiencing significant growth with the increasing adoption of large language models (LLMs) for programming tasks. Current research is focused on improving the accessibility and effectiveness of LLM-generated code, particularly for beginning programmers. A major challenge in this area is the comprehension of LLM-generated code, with studies showing that beginners struggle to understand and evaluate the correctness of such code. To address this issue, researchers are exploring new methods for generating high-quality code comments and pre-training datasets. The development of comprehensive benchmarks and evaluation frameworks is also a key area of research, enabling more systematic and representative assessments of LLM performance. Furthermore, innovations in data generation and synthesis are allowing for the creation of larger, more diverse datasets, which is essential for advancing code intelligence tasks. Noteworthy papers in this area include:

• A study on the challenges faced by beginning programmers in understanding LLM-generated code, highlighting the need for improved code comprehension and evaluation techniques.
• Research on rebuilding pre-training datasets with LLM-generated comments, demonstrating improved model performance in code summarization, generation, and translation tasks.
• The introduction of a comprehensive code benchmark for multi-task LLM evaluation, providing a holistic and objective assessment of model strengths and weaknesses.
• The development of a novel pipeline for generating software engineering training data at scale, enabling the creation of larger, more diverse datasets and advancing the state-of-the-art in automated software engineering.