The field of software engineering is witnessing significant advancements with the integration of large language models (LLMs) in various aspects of code analysis and generation. Recent developments indicate a shift towards improving the evaluation and testing of LLMs in real-world engineering scenarios. Researchers are working on creating more comprehensive and configurable benchmarks to assess the capabilities of LLMs in diverse scenarios, such as code generation, bug fixing, and test-driven development. Additionally, there is a growing interest in leveraging LLMs for detecting semantic conflicts and improving test case generation, which can lead to more reliable and efficient software development processes.

Noteworthy papers in this area include:

• CoreCodeBench, which introduces a novel, configurable multi-scenario repository-level benchmark for evaluating LLMs in real-world engineering projects, providing insights into their capabilities and limitations.
• A proposal for a new heuristic for detecting the Eager Test smell, which aims to improve the accuracy of detection rules and address practitioners' concerns regarding existing methods.
• Leveraging LLMs for semantic conflict detection via unit test generation, which explores the potential of LLMs in improving test case generation and conflict detection in software development.
• Rethinking verification for LLM code generation, which proposes a human-LLM collaborative method for enhancing test case generation and improving the evaluation of LLMs in code generation tasks.