Integral使創成式AI代碼審查工具開源

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本周啟動了一個開源的Robin AI項目，該項目利用OpenAI的生成預訓練轉換器（GPT）平台來審查代碼更改并提供建設性的回報。

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是醫療保健提供者使用的資料分析平台。該公司最初使用一組Bash腳本開發Robin AI，以建立一個生成人工智能（AI）工具來協助其内部開發團隊。

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首席技術官約翰·庫恩（John Kuhn）表示，Robin AI本質上是一個機器人，其功能就像開發人員的教練一樣。Kuhn說，它首先審查代碼，然後提出開發人員可以立即應用的優化建議。他補充說，由每個開發人員決定是否接受這些建議，但Integral已經在使用它來確定所有正在編寫的代碼都是人類可讀的。

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 代碼存儲庫上運作得最好，但可以應用于駐留在 Git 存儲庫中的任何代碼庫。Integral還開發了GitHub Actions腳本。

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，作為一個開源項目，Robin AI可以與任意數量的生成AI平台內建，這些平台可能使用更多特定于領域的大型語言模型（LLM）建構，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.

大多數開發人員已經在使用GitHub Copilot等AI工具來編寫更好的代碼。Robin AI現在正在将生成AI功能擴充到代碼審查過程，作為提高所編寫代碼品質的努力的一部分。實際上，它提供了一種方法來顯示開發人員所犯的常見錯誤，并使他們能夠在同僚發現這些錯誤之前修複這些錯誤。

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動應用程式開發，但庫恩表示，他認為整個軟體工程過程自動化隻是時間問題。他補充說，随着這些進步的取得，建構應用程式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.

當然，并非應用程式開發社群中的每個人都同意這一評估。但有一點是明确的，那就是生成式人工智能功能将很快普及。事實上，下一個前沿領域可以說是将生成式AI平台與架構內建，這些架構可以在整個企業IT環境中自動應用生成式AI平台提出的建議和建議。

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.

與此同時，DevOps團隊應該根據生成式AI技術今天可以提供的功能和未來的潛力來評估它們。許多使應用程式開發和部署變得乏味的手動任務将變得越來越自動化。緻力于無情地自動化盡可能多的流程的 DevOps 團隊自然會處于采用的最前沿。挑戰和機遇在于确定人工智能平台是否可以信任，或者人類是否必須始終處于循環中以確定沒有意外結果。

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.

畢竟，雖然人工智能可以應用于自動化部署，但如果犯了錯誤，它們是否可以復原還不清楚。