Prompting has become one of the main approaches to leverage emergent
capabilities of Large Language Models [Brown et al. NeurIPS 2020, Wei et al.
TMLR 2022, Wei et al. NeurIPS 2022]. During the last year, researchers and
practitioners have been playing with prompts to see how to make the most of
LLMs. By homogeneously dissecting 80 papers, we investigate in deep how
software testing and verification research communities have been abstractly
architecting their LLM-enabled solutions. More precisely, first, we want to
validate whether downstream tasks are an adequate concept to convey the
blueprint of prompt-based solutions. We also aim at identifying number and
nature of such tasks in solutions. For such goal, we develop a novel downstream
task taxonomy that enables pinpointing some engineering patterns in a rather
varied spectrum of Software Engineering problems that encompasses testing,
fuzzing, debugging, vulnerability detection, static analysis and program
verification approaches.

通过细致地剖析 80 篇论文，我们研究了软件测试和验证研究社区如何抽象构建基于大型语言模型（LLMs）的解决方案。我们验证了下游任务是否能够传达基于提示的解决方案的蓝图，并确定了解决方案中的任务数量和性质。为此，我们开发了一种新的下游任务分类方法，能够在涵盖测试、模糊测试、调试、漏洞检测、静态分析和程序验证方法的软件工程问题的多样化光谱中准确定位一些工程模式。