Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities. However, existing benchmarks predominantly focused on simplified or isolated aspects of coding, such as single-file code generation or repository issue debugging, falling short of measuring the full spectrum of challenges raised by real-world programming activities. In this case study, we explore the performance of LLMs across the entire software development lifecycle with DevEval, encompassing stages including software design, environment setup, implementation, acceptance testing, and unit testing. DevEval features four programming languages, multiple domains, high-quality data collection, and carefully designed and verified metrics for each task. Empirical studies show that current LLMs, including GPT-4, fail to solve the challenges presented within DevEval. Our findings offer actionable insights for the future development of LLMs toward real-world programming applications.

In recent years, large language models (LLMs) such as GPT-4 have demonstrated impressive potential in a wide range of fields, including biology, genomics and healthcare. Numerous studies have attempted to apply pre-trained LLMs to single-cell data analysis within one tissue. However, when it comes to cross-tissue cell annotation, LLMs often suffer from unsatisfactory performance due to the lack of specialized biological knowledge regarding genes and tissues. In this paper, we introduce scRAG, a novel framework that incorporates advanced LLM-based RAG techniques into cross-tissue single-cell annotation. scRAG utilizes LLMs to retrieve structured triples from knowledge graphs and unstructured similar cell information from the reference cell database, and it generates candidate cell types. The framework further optimizes predictions by retrieving marker genes from both candidate cells and similar cells to refine its results. Extensive experiments on a cross-tissue dataset demonstrate that our scRAG framework outperforms various baselines, including generalist models, domain-specific methods, and trained classifiers. The source code is available at https://github.com/YuZhiyin/scRAG.