Large Language Models (LLMs) have drawn widespread attention and research due to their astounding performance in tasks such as text generation and reasoning. Derivative products, like ChatGPT, have been extensively deployed and highly sought after. Meanwhile, the evaluation and optimization of LLMs in software engineering tasks, such as code generation, have become a research focus. However, there is still a lack of systematic research on the application and evaluation of LLMs in the field of software engineering. Therefore, this paper is the first to comprehensively investigate and collate the research and products combining LLMs with software engineering, aiming to answer two questions: (1) What are the current integrations of LLMs with software engineering? (2) Can LLMs effectively handle software engineering tasks? To find the answers, we have collected related literature as extensively as possible from seven mainstream databases, and selected 123 papers for analysis. We have categorized these papers in detail and reviewed the current research status of LLMs from the perspective of seven major software engineering tasks, hoping this will help researchers better grasp the research trends and address the issues when applying LLMs. Meanwhile, we have also organized and presented papers with evaluation content to reveal the performance and effectiveness of LLMs in various software engineering tasks, providing guidance for researchers and developers to optimize.

翻译：大型语言模型（LLMs）因其在文本生成和推理等任务中的惊人表现而受到广泛关注和研究。诸如ChatGPT等衍生品已被广泛部署并备受追捧。同时，LLMs在代码生成等软件工程任务中的评估与优化已成为研究热点。然而，目前对于LLMs在软件工程领域的应用与评估仍缺乏系统性研究。因此，本文首次全面调查和整理了LLMs与软件工程相结合的研究及产品，旨在回答两个问题：（1）当前LLMs与软件工程的融合现状如何？（2）LLMs能否有效处理软件工程任务？为寻找答案，我们尽可能全面地收集了来自七个主流数据库的相关文献，并筛选出123篇论文进行分析。我们对这些论文进行了详细分类，并从七大类软件工程任务的角度审视了LLMs的研究现状，期望帮助研究者更好地把握研究趋势并解决应用LLMs时遇到的问题。同时，我们还整理并呈现了包含评估内容的论文，以揭示LLMs在各种软件工程任务中的性能与有效性，为研究人员和开发者的优化工作提供指导。