End-to-End ML-Driven Feedback Loops in DevOps Pipelines

Abstract

DevOps is the new approach in software development that has encouraged interaction between the development and operation
teams. DevOps also involves using feedback mechanisms that enhance continuous and rapid cycle feedback. However, what
often occurs is that these feedback loops must be managed manually, which takes time and can be prone to mistakes. This paper
explores AI and ML’s ability to perform feedback loops in DevOps pipelines. In the context of the current study, we investigate
the application of big data and real-time monitoring to improve code quality problem detection and prediction of performance
consequences. We illustrate how feedback to developers about the necessary changes can be provided through ML models to
analyze data obtained from different sources like application logs, monitoring tools, and user interactions. The paper explains
the basics needed to establish feedback loops based on ML, which include data acquisition, data cleaning, model training, and
online prediction. We also discuss the issues and concerns when using AI/ML in DevOps, such as the model’s interpretability,
the tool’s integration, and change management. In this article, we explain how AI can make feedback loops smarter and provide
case studies and real-life examples of how this can help improve code quality, solve problems more quickly, and enhance the
relationship between development and operations. Last but not least, we discuss potential research directions for this area’s
further development, such as approaches to improve model interpretability, integrating collaborative learning into the DevOps
process, and creating reference models for AI adoption in DevOps pipelines. In this paper, we use AI and ML to map out ways
for organizations to improve their DevOps processes and ensure a feedback loop at every stage.