From 21ba3b514c7182451b245dad75f8c471407aefb6 Mon Sep 17 00:00:00 2001 From: David Date: Thu, 23 May 2024 10:48:45 +0800 Subject: [PATCH] =?UTF-8?q?=E5=9F=BA=E4=BA=8Ewechaty=E7=9A=84=E8=BD=AF?= =?UTF-8?q?=E4=BB=B6=E5=BC=80=E5=8F=91=E5=9C=BA=E6=99=AF=E5=9C=A8AI?= =?UTF-8?q?=E5=8A=A0=E6=8C=81=E4=B8=8B=E7=9A=84=E6=9E=84=E5=BB=BA?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- jekyll/_contributors/zhichunxiao.md | 12 ++++ ...based-on-wechaty-with-the-support-of-ai.md | 61 ++++++++++++++++++ .../article-picture.webp | Bin 0 -> 535299 bytes .../avatar.webp | Bin 0 -> 101741 bytes .../devops.webp | Bin 0 -> 8372 bytes .../plan-and-code.webp | Bin 0 -> 50559 bytes .../contributors/zhichunxiao/avatar.png | Bin 0 -> 101741 bytes 7 files changed, 73 insertions(+) create mode 100644 jekyll/_contributors/zhichunxiao.md create mode 100644 jekyll/_posts/2024-05-23-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai.md create mode 100644 jekyll/assets/2024/05-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai/article-picture.webp create mode 100644 jekyll/assets/2024/05-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai/avatar.webp create mode 100644 jekyll/assets/2024/05-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai/devops.webp create mode 100644 jekyll/assets/2024/05-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai/plan-and-code.webp create mode 100644 jekyll/assets/contributors/zhichunxiao/avatar.png diff --git a/jekyll/_contributors/zhichunxiao.md b/jekyll/_contributors/zhichunxiao.md new file mode 100644 index 000000000..07323824b --- /dev/null +++ b/jekyll/_contributors/zhichunxiao.md @@ -0,0 +1,12 @@ +--- +name: zhichunxiao +site: https://github.com/zhichunxiao +avatar: /assets/contributors/zhichunxiao/avatar.png +bio: 一个狂热的 AI 玩家 +email: heroesop@126.com +--- + +## Contact me + +- Github: +- Email: \ No newline at end of file diff --git a/jekyll/_posts/2024-05-23-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai.md b/jekyll/_posts/2024-05-23-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai.md new file mode 100644 index 000000000..a42eaeec1 --- /dev/null +++ b/jekyll/_posts/2024-05-23-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai.md @@ -0,0 +1,61 @@ +--- +title: "基于wechaty的软件开发场景在AI加持下的构建" +author: David +categories: article +tags: + - ai + - gpt-4 + - automation + - chatbot +image: /assets/2024/05-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai/article-picture.webp +--- + +> 作者: [David], A fanatical AI player. + +随着大模型的发展,在AI辅助编程的领域的关注热区已经从代码生成任务,扩展到很多其他开发任务,例如自动生成测试用例、bug 自动修复等。同时,作为上游的需求工程也成为热议的话题。然而,在把程序员从繁重的编码劳动中解放这个伟大的事情落地的过程中,对实际工作场景的重构至关重要,而当下的关注度却远不如AI研究工作本身。 + +## 为什么软件开发中场景很重要? + +我们从一个简单问题说起。GPT-4出现至今,在辅助程序员编程任务上仍然强于代码大模型产品,为什么AI编程需要代码大模型产品,而不是直接用GPT-4?首先,代码大模型工具可以捕获代码上下文数据,可以支持多文件的数据输入,在代码理解能力的提升上具有可扩展性。其次,程序员在编码的时候特别需要连贯性,这种顺畅的编码是极好的用户体验,复制粘贴这种操作是他们不喜欢的。代码大模型产品是以插件的形式嵌入IDE中的,而直接使用GPT-4虽然能力很强,但是没在实际开发场景中,因此没能很好的发挥作用。最后,融入场景能更好的沉淀数据、计算指标、迭代优化。 +同样,在编码的上游是Plan阶段,也就是需求工程,目前已经有很多能力可以由大模型完成,但如果不把这样的能力以工具或产品的形态放到实际场景中,那也很难发力。在需求讨论的过程中,这个场景就是在即时聊天工具中的需求讨论。对于多数国内的C端开发者,微信作为最主流的及时聊天工具,如果能把需求讨论的场景梳理清楚,把大模型的能力嫁接进来,对于需求讨论的提效是不可估量的,而wechaty就是这样一个极好的辅助工具。 +在扩展人机协作方式中,场景下的用户需求指引AI研究方向,研究成果嫁接到工作场景,只有从场景的角度思考,才有可能打破现有功能边界,产生非线性的效能提升。 + +## 如何基于wechaty打造AI赋能的软件开发场景 + +### 软件开发工作流程是什么? + +Devops 模式是当下软件开发中典型的最佳实践,通常情况下包括从需求设计、编码、持续集成到持续交付,再通过反馈环路持续学习,回馈到初始的需求设计阶段,形成闭环并持续迭代。在整个环路中,不但需要各阶段的工程实践,还需要实时高效的沟通协作,才能保障业务目标落地。 + +![devops.webp](/assets/2024/05-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai/devops.webp) + +### 如何利用AI加持各个阶段的工程实践? + +在Plan阶段,需求获取、需求拆分、需求分类、需求排序等场景,是需求工程中可以通过大模型完成的任务,相关人员可以通过群聊天记录自动整理需求(例如:@wechaty/summarize,默认把群聊天中的信息自动梳理成需求条目),再把这些需求分类(例如:@wechaty/classify fun,按功能需求和非功能需求区分)。执行这些操作通过调用类似GPT-4接口实现。 + +在Code阶段,代码生成、测试用例生成、bug自动修复、代码解释等场景是目前被众多代码大模型产品优先实现的任务,也是当前AI编码关注的热区。然而,从场景考虑需要把各个任务串联,上一级的输出作为下一级的输入。 + +![plan-and-code.webp](/assets/2024/05-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai/plan-and-code.webp) + +无论是Plan阶段还是Code阶段,如果结果不符合预期,需要调整后方再进入下一阶段,这一点非常必要,因为大模型是概率模型,几个概率相乘下来结果很可能偏离预期。可以发起多轮对话做调整,或直接手工修正,这恰好是wechaty依托微信群这个实时沟通场景中发挥价值的地方。 + +另外,当前的代码大模型产品的Chat功能,从场景上看有其局限性,一是只能开发者和Chatbot点对点对话,无法形成多人协作;二是Chat功能的自然语言描述来自于上游需求讨论的结果,在IDE开发场景下无法自动导入,因此需要wechaty这样的外部工具去做从Plan到Code的打通。 + +最后,更重要的是,当前学术界在需求工程讨论中,有一个痛点是需求数据隐匿性强,且获取成本高。如果从场景出发,基于wechaty和微信群生态很容易沉淀下来实际开发场景的数据,对学术界在需求工程的研究上可以给予很大支持。与此同时,学术界所需的数据信息可以反馈给wechaty社区,在真实开发场景中获取。 + +### 如何利用AI助力实时高效的沟通协作? + +软件开发过程中会出现大量的沟通和协作,例如需求讨论、故障定位,社群运营等。这些成本评估下来往往比实际编码更高,因此如果AI能在具体场景中解决痛点问题,则带来的价值更大。可以基于wechaty构建的场景能力和解决的痛点,详见下表。 + +|**基于wechaty打造场景能力**|**痛点**| +|------|------| +|自动拉群并添加干系人 |我是个“拉群工程师” | +|自动生成群消息摘要 |我刚进群,你们讨论了啥? | +|自动得出结论 |开会没结果,事情不了了之 | +|群聊天备份 |之前为那个事讨论那么久,聊啥来着? | +|群解散 |群太多 | + +需要注意的是,一个功能可以用在很多场景中,比如,自动拉群这个功能可以在需求讨论分解任务后,按各任务所指派的人批量建群;在故障定位时,把运行时错误代码做关联性分析,相关开发人员自动拉到一个群里。与此同时,一个场景可能需要多个功能支持,比如,一个问题解决后,群的运营周期结束,需要自动备份群聊天,作为数据和知识的沉淀,同时自动解散该群。 + +## 基于wechaty的AI编程框架 + +综上所述,这个项目考虑把AI大模型在软件开发全过程中已经被验证过的能力先用起来,利用wechaty和微信生态,在实际开发场景中让开发者感受到AI在效能提升上的价值,逐渐把各个场景连接,沉淀数据、不断优化,让价值更自动的流动。 \ No newline at end of file diff --git a/jekyll/assets/2024/05-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai/article-picture.webp b/jekyll/assets/2024/05-construction-of-software-development-scenarios-based-on-wechaty-with-the-support-of-ai/article-picture.webp new file mode 100644 index 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