ChatOps for microservice systems: A low-code approach using service composition and large language models

Abstract

The Microservice Architecture (MSA) plays a pivotal role in contemporary e-business, promoting service independence, autonomy, and continual evolution in line with the principles of DevOps. However, the distributed nature of the MSA introduces additional complexity, which requires familiarity with multiple DevOps (Development and Operations) tools, thereby increasing the learning curve. This paper presents a specialized ChatOps (Chat Operations) approach that allows MSA developers to compose new ChatOps capabilities in a low-code way (i.e., with minimal coding). The proposed ChatOps4Msa approach leverages established ChatOps functionalities to facilitate the real-time monitoring of service status, conduct service testing, track issues, and receive alerts using natural language or the proposed ChatOps Query Language (CQL). The use of large language models (LLMs) for functional intents also enhances the usability of the DevOps toolchain in microservices systems to streamline implementation.