Integral使创成式AI代码审查工具开源

Integral this week launched an open source Robin AI project that leverages OpenAI’s generative pre-trained transformer (GPT) platform to review code changes and provide constructive feedback.

Integral本周启动了一个开源的Robin AI项目，该项目利用OpenAI的生成预训练转换器（GPT）平台来审查代码更改并提供建设性的反馈。

Integral is a data analytics platform used by health care providers. The company originally developed Robin AI using a set of Bash scripts to create a generative artificial intelligence (AI) tool to assist its internal development team.

Integral是医疗保健提供者使用的数据分析平台。该公司最初使用一组Bash脚本开发Robin AI，以创建一个生成人工智能（AI）工具来协助其内部开发团队。

Integral CTO John Kuhn said Robin AI is essentially a bot that functions like a coach for developers. It first reviews code and then surfaces optimization suggestions that developers can instantly apply, said Kuhn. It’s up to each developer to decide whether to accept those suggestions, but Integral is already using it to, for example, make sure all the code being written is human readable, he added.

Integral首席技术官约翰·库恩（John Kuhn）表示，Robin AI本质上是一个机器人，其功能就像开发人员的教练一样。Kuhn说，它首先审查代码，然后提出开发人员可以立即应用的优化建议。他补充说，由每个开发人员决定是否接受这些建议，但Integral已经在使用它来确保所有正在编写的代码都是人类可读的。

Robin AI currently works best on JavaScript code repositories, but can be applied to any codebase residing in a Git repository. Integral has also already developed a GitHub Actions script.

Robin AI 目前在 JavaScript 代码存储库上运行得最好，但可以应用于驻留在 Git 存储库中的任何代码库。Integral还开发了GitHub Actions脚本。

In addition, as an open source project, Robin AI can be integrated with any number of generative AI platforms that might be built using more domain-specific large language models (LLMs), noted Kuhn.

此外，作为一个开源项目，Robin AI可以与任意数量的生成AI平台集成，这些平台可能使用更多特定于领域的大型语言模型（LLM）构建，Kuhn指出。

Most developers are already using AI tools such as GitHub Copilot to write better code. Robin AI is now extending generative AI capabilities to the code review process as part of an effort to improve the quality of the code being written. In effect, it provides a means to surface common mistakes developers make and enables them to fix those before they are discovered by a colleague.

大多数开发人员已经在使用GitHub Copilot等AI工具来编写更好的代码。Robin AI现在正在将生成AI功能扩展到代码审查过程，作为提高所编写代码质量的努力的一部分。实际上，它提供了一种方法来显示开发人员所犯的常见错误，并使他们能够在同事发现这些错误之前修复这些错误。

It’s not clear how much application development will be driven by AI, but Kuhn said he believes it’s only a matter time before the entire software engineering process is automated. As those advances are made, the cost of building applications will effectively drop to zero, he added.

目前尚不清楚人工智能将在多大程度上推动应用程序开发，但库恩表示，他认为整个软件工程过程自动化只是时间问题。他补充说，随着这些进步的取得，构建应用程序的成本将有效地降至零。

Of course, not everyone in the application development community agrees with that assessment. But one thing that is clear is that generative AI capabilities will soon be pervasive. In fact, the next frontier will arguably involve integrating generative AI platforms with frameworks that make it possible to automatically apply suggestions and recommendations made by generative AI platforms across an entire enterprise IT environment.

当然，并非应用程序开发社区中的每个人都同意这一评估。但有一点是明确的，那就是生成式人工智能功能将很快普及。事实上，下一个前沿领域可以说是将生成式AI平台与框架集成，这些框架可以在整个企业IT环境中自动应用生成式AI平台提出的建议和建议。

In the meantime, DevOps teams should be evaluating generative AI technologies in terms of the capabilities they can provide today and their future potential. Many of the manual tasks that conspire to make application development and deployment tedious will become increasingly automated. DevOps teams committed to ruthlessly automating as many processes as possible will naturally be at the forefront of adoption. The challenge and the opportunity lies in determining whether AI platforms can be trusted or if a human must always be in the loop to ensure there are no unexpected outcomes.

与此同时，DevOps团队应该根据生成式AI技术今天可以提供的功能和未来的潜力来评估它们。许多使应用程序开发和部署变得乏味的手动任务将变得越来越自动化。致力于无情地自动化尽可能多的流程的 DevOps 团队自然会处于采用的最前沿。挑战和机遇在于确定人工智能平台是否可以信任，或者人类是否必须始终处于循环中以确保没有意外结果。

After all, while AI can be applied to automate deployments, it’s not quite as clear whether they can be rolled back if a mistake is made.

毕竟，虽然人工智能可以应用于自动化部署，但如果犯了错误，它们是否可以回滚还不清楚。