The field of large language models (LLMs) for code reasoning and generation is rapidly advancing, with a focus on evaluating and improving the semantic understanding and reasoning capabilities of these models. Recent studies have highlighted the importance of benchmarking LLMs on fundamental static analysis tasks, such as data dependency, control dependency, and information flow, to assess their ability to understand program semantics. Additionally, research has explored the use of LLMs for evaluating code quality attributes, such as readability, and has shown that they can provide standardized and consistent evaluations.

Furthermore, the integration of LLMs with other approaches, such as Programming by Example (PBE), has demonstrated potential for improving code generation and transformation tasks. The development of new frameworks, such as the Code Triangle, has also provided a systematic approach to evaluating LLMs across multiple dimensions, including editorial analysis, code implementation, and test case generation.

Notable papers in this area include: CORE, which presents a benchmark for evaluating LLMs on static analysis tasks and highlights the challenges of complex control structures and backward dependency patterns. PBE Meets LLM, which evaluates the performance of LLMs on PBE tasks and proposes a hybrid approach that combines the strengths of LLMs and traditional PBE solvers. Evaluating Efficiency and Novelty of LLM-Generated Code for Graph Analysis, which presents a systematic study of LLMs' ability to generate efficient C implementations of graph-analysis routines and confirms that contemporary LLMs excel at optimizing and integrating established algorithms.