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28 October 2024
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Last updated on
28 October 2024 |
G. Lanciano, M. Stein, V. Hilt, T. Cucinotta. "Analyzing Declarative Deployment Code with Large Language Models," in Proceedings of the 13th International Conference on Cloud Computing and Services Science (CLOSER 2023), April 26-28, 2023, Prague, Czech Republic.
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 time-consuming 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.
Copyright by SCITEPRESS.
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