用大模型分析技术博客,揭示工业界如何用大模型做开发和部署。
Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models
- 用大模型自动分析1152篇行业博客,分类总结应用场景。
- 代码生成是主流,但大模型也用于理解、摘要与接口推荐。
- 关注云端部署,正兴起轻量化模型在移动端的落地。
大型语言模型等基础模型(FMs)已深刻影响软件工程(SE)。SE与FMs的交互催生了将FMs融入软件工程实践(FM4SE)以及用软件工程方法优化FMs(SE4FM)的趋势。现有研究多聚焦学术成果,本文首次从工业界视角出发,分析来自领先科技公司的155篇FM4SE与997篇SE4FM博客,采用大模型驱动的调研方法系统标注并总结讨论内容。结果显示,代码生成是最常见的FM4SE任务,但大模型也被广泛用于代码理解、摘要生成和API推荐。多数SE4FM博客关注模型部署与运维、系统架构与编排。尽管以云部署为主,但对模型压缩及在边缘/移动设备上部署的兴趣正在增长。基于洞察,本文提出八个未来研究方向,旨在弥合学术与实际应用间的差距。研究不仅丰富了FM4SE与SE4FM的实践知识,还验证了大模型在技术与灰色文献调研中的高效性。数据集、结果、代码与提示词均公开于https://github.com/SAILResearch/fmse-blogs。
原文摘要 · Abstract (English)
Foundation models (FMs) such as large language models (LLMs) have significantly impacted many fields, including software engineering (SE). The interaction between SE and FMs has led to the integration of FMs into SE practices (FM4SE) and the application of SE methodologies to FMs (SE4FM). While several literature surveys exist on academic contributions to these trends, we are the first to provide a practitioner's view. We analyze 155 FM4SE and 997 SE4FM blog posts from leading technology companies, leveraging an FM-powered surveying approach to systematically label and summarize the discussed activities and tasks. We observed that while code generation is the most prominent FM4SE task, FMs are leveraged for many other SE activities such as code understanding, summarization, and API recommendation. The majority of blog posts on SE4FM are about model deployment & operation, and system architecture & orchestration. Although the emphasis is on cloud deployments, there is a growing interest in compressing FMs and deploying them on smaller devices such as edge or mobile devices. We outline eight future research directions inspired by our gained insights, aiming to bridge the gap between academic findings and real-world applications. Our study not only enriches the body of knowledge on practical applications of FM4SE and SE4FM but also demonstrates the utility of FMs as a powerful and efficient approach in conducting literature surveys within technical and grey literature domains. Our dataset, results, code and used prompts can be found in our online replication package at https://github.com/SAILResearch/fmse-blogs.
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