  The advent of Industrial Internet of Things (IIoT) has imposed more stringent
requirements on industrial software in terms of communication delay,
scalability, and maintainability. Microservice architecture (MSA), a novel
software architecture that has emerged from cloud computing and DevOps,
presents itself as the most promising solution due to its independently
deployable and loosely coupled nature. Currently, practitioners are inclined to
migrate industrial legacy systems to MSA, despite numerous challenges it
presents. In this paper, we propose an automated microservice decomposition
method for extracting microservice candidates based on spectral graph theory to
address the problems associated with manual extraction, which is
time-consuming, labor intensive, and highly subjective. The method is divided
into three steps. Firstly, static and dynamic analysis tools are employed to
extract dependency information of the legacy system. Subsequently, information
is transformed into a graph structure that captures inter-class structure and
performance relationships in legacy systems. Finally, graph-based clustering
algorithm is utilized to identify potential microservice candidates that
conform to the principles of high cohesion and low coupling. Comparative
experiments with state of-the-art methods demonstrate the significant
advantages of our proposed method in terms of performance metrics. Moreover,
Practice show that our method can yield favorable results even without the
involvement of domain experts.
