From 4b2a07af5404ffd985643fc771f4075d7d8a642f Mon Sep 17 00:00:00 2001 From: Phodal Huang Date: Sat, 13 Jul 2024 14:17:30 +0800 Subject: [PATCH] =?UTF-8?q?feat(docs):=20=E6=89=A9=E5=B1=95AI=E5=9C=A8?= =?UTF-8?q?=E8=BD=AF=E4=BB=B6=E5=B7=A5=E7=A8=8B=E5=BA=94=E7=94=A8=E7=9A=84?= =?UTF-8?q?=E5=85=A8=E7=90=83=E8=A7=86=E8=A7=92?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 扩展了AI在软件工程应用的全球视角,强调了不同地区对AI工具的关注点差异,以及AI技术如何从辅助开发人员发展到涵盖软件开发的全生命周期。 --- src/ai4se.md | 23 ++++++++++++++++++++++- src/images/ai-native-rd-tool.png | Bin 0 -> 742902 bytes 2 files changed, 22 insertions(+), 1 deletion(-) create mode 100644 src/images/ai-native-rd-tool.png diff --git a/src/ai4se.md b/src/ai4se.md index d8015d4..24291ac 100644 --- a/src/ai4se.md +++ b/src/ai4se.md @@ -1,13 +1,17 @@ # 理解生成式 AI 在软件工程的应用 -站在 2024 来看,与 2023 年相比,我们会发现 AI 在软件工程中的应用会更加广泛。这种变化也体现在了 AI 编程工具上,从我们年初的分析来看,其可以总结为: +从 2024 年的视角回顾,与 2023 年相比,AI 在软件工程中的应用已经变得更加广泛和深入。这一趋势体现在AI编程工具的进化上,主要体现在以下几个方面: 1. 全面探索:从辅助开发人员到全生命周期 2. 演进路径:个体、团队、组织。 3. 形态变化:从 IDE 插件到智能 IDE +站在全球来看,在不同的国家、区域人们的关注点是不一样的,比如在中国,人们更关注于如何提高软件工程师的工作效率,而在其它一些区域,人们更关注于如何 +提高软件工程的质量、如何辅助进行遗留系统的迁移。 除了各自所处的数字化阶段、水平不同,还存在一些技术人才数量、质量、分布等方面的差异。 + ## 全面探索:从辅助开发人员到全生命周期 +AI 技术已经从简单的辅助开发人员发展到涵盖软件开发的整个生命周期。在这一过程中,AI 工具的应用范围不断扩展,从需求分析到运维管理,每个阶段都得到了显著提升。 ### 单工具 Copilot @@ -26,6 +30,23 @@ 就 2023 年的结论而言,基于人工智能的工具与基础大语言模型可以增强软件开发在设计、 需求、测试、发布和运维等各个环节中的能力,提高质量和效率。 但是,这些工具往往是破碎、割裂的,还可能并不适合我们现有的研发流程。 +### AI 原生的研发工具 + +在市场上,我们也可以看到市面上的主流研发工具,如 JetBrains、GitHub(网站)等,都在逐渐加入 AI 功能,使得 AI 功能逐渐融入到我们的日常工作中。 + +![](images/ai-native-rd-tool.png) + +在 IntelliJ IDEA 中,我们可以看到 AI 功能的加入,如: + +- 原生的向量化模型 +- 基于语义化搜索(SearchEverywhere) +- 结合补全统计的机器学习补全插件:Machine Learning Code Completion +- 适用于单个代码行的 Full Line Code Completion +- 等等 + +而除了 GitHub Copilot 工具本身,其也开放了其插件能力,使得我们可以定义自己的 AI +智能体,以适应我们自己的工作流程:[Chat extensions](https://code.visualstudio.com/api/extension-guides/chat) + ### 多阶段协同 在 2024 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