Analyzing Declarative Deployment Code with Large Language Models.

In the cloud-native era, developers have at their disposal an unprecedented landscape of services to build scalable distributed systems.The DevOps paradigm emerged as a response to the increasing necessity of better automations, capable of dealing with the complexity of modern cloud systems.For instance, Infrastructure-as-Code tools provide a declarative way to define, track, and automate changes to the infrastructure underlying a cloud application.Assuring the quality of this part of a code base is of utmost importance.However, learning to produce robust deployment specifications is not an easy feat, and for the domain experts it is timeconsuming to conduct code-reviews and transfer the appropriate knowledge to novice members of the team.Given the abundance of data generated throughout the DevOps cycle, machine learning (ML) techniques seem a promising way to tackle this problem.In this work, we propose an approach based on Large Language Models to analyze declarative deployment code and automatically provide QA-related recommendations to developers, such that they can benefit of established best practices and design patterns.We developed a prototype of our proposed ML pipeline, and empirically evaluated our approach on a collection of Kubernetes manifests exported from a repository of internal projects at Nokia Bell Labs.