Large Language Models (LLMs) have drawn widespread attention and research due to their astounding performance in text generation and reasoning tasks. 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 applying and evaluating LLMs in software engineering. Therefore, this paper 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 timely papers published starting from 2022 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, guiding researchers and developers to optimize.

翻译：大语言模型（LLMs）因其在文本生成与推理任务中的惊人表现而受到广泛关注与研究。以ChatGPT为代表的衍生产品已被大规模部署并备受追捧。与此同时，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，目前关于LLMs在软件工程中应用与评估的系统性研究仍显不足。为此，本文全面调研与梳理了LLMs与软件工程相结合的研究与产品，旨在回答两个核心问题：（1）当前LLMs与软件工程有哪些结合方式？（2）LLMs能否有效处理软件工程任务？为寻求答案，我们从七个主流数据库中尽可能广泛地收集了相关文献，并筛选出123篇自2022年以来发表的时效性论文进行分析。我们对这些论文进行了细致分类，并从七大软件工程任务的视角综述了LLMs的研究现状，以帮助研究者更好地把握研究趋势并应对LLMs应用中的问题。同时，我们还整理并呈现了包含评估内容的论文，以揭示LLMs在不同软件工程任务中的表现与效能，从而指导研究者与开发者进行优化。