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 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 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.
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.
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.
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.
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.
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.
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.
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.
