Remodularization Analysis for Microservice Discovery Using Syntactic and Semantic Clustering

Abstract

This paper addresses the challenge of automated remodularization of large systems as microservices. It focuses on the analysis of enterprise systems, which are widely used in corporate sectors and are notoriously large, monolithic and challenging to manually decouple because they manage asynchronous, user-driven business processes and business objects (BOs) having complex structural relationships. The technique presented leverages semantic knowledge of enterprise systems, i.e., BO structure, together with syntactic knowledge of the code, i.e., classes and interactions as part of static profiling and clustering. On a semantic level, BOs derived from databases form the basis for prospective clustering of classes as modules, while on a syntactic level, structural and interaction details of classes provide further insights for module dependencies and clustering, based on K-Means clustering and optimization. Our integrated techniques are validated using two open source enterprise customer relationship management systems, SugarCRM and ChurchCRM. The results demonstrate improved feasibility of remodularizing enterprise systems (inclusive of coded BOs and classes) as microservices. Furthermore, the recommended microservices, integrated with 'backend' enterprise systems, demonstrate improvements in key non-functional characteristics, namely high execution efficiency, scalability and availability.

Microservice Remodularisation of Monolithic Enterprise Systems for Embedding in Industrial IoT Networks

“…Again, in this matrix, rows correspond to methods, and columns correspond to unique words identified in step four of the algorithm that appear in the corresponding methods. The values in mtduwcount are then used in the seventh step to calculate the cosine similarity between the methods using function COSINECAL (lines [12][13][14].…”

“…The feature set data in variables mtdborel, mtdcosine, mtdrel and the BOs obtained from Algorithm 1 are provided as input to the K-Means algorithm to cluster the methods related to BOs based on their syntactic and semantic relationships. We followed a similar approach in our previous work [14], in which we adapted class-level relationships for microservice cluster discovery. However, here we have moved to the next level of system analysis by evaluating method level relationships.…”

“…As such, in Algorithm 1, each dataset captures different aspects of relationships between the methods in the given system (Figure 4). Finally, as per our earlier work [14], we configured the K-Means algorithm to produce a set of clusters that group the methods of the analysed enterprise system as recommendations for constructing microservices.…”

Facilitating the migration to the microservice architecture via model-driven reverse engineering and reinforcement learning

“…Traditional software systems may contain very large modules, therefore, identifying and extracting the individual instructions that compose a fine-grained module from these code-bases is not a trivial task [22,29]. Although significant research has been conducted about decomposing software into MSs, the extraction of MSs by decoupling and reusing pieces of a bigger system, through refactoring and remodularization, is still an open problem [10]. We argue that a high-level model of the system can aid the decomposition of a large codebase because it enables focusing on the important aspects of the system while removing the unessential information.…”

“…previous work, where microservices are typically associated with classes or packages of the initial codebase. Fine-grained method-based migration is sometimes mentioned as part of future work in these proposals [10].…”