Service-Oriented Architecture has been widely adopted in software development because of its properties of addressing distributed and heterogeneous environments. At the same time, the highly-demanding flexibility and capacity of adaptation in software projects introduce a process of constant code modifications and complexity growth. Thus, developers quickly make mistakes such as code duplication or unnecessary code, negatively impacting quality attributes such as performance and maintainability. Those modifications are usually conducted by refactoring, which significantly improves software quality. In this context, we aim to compare manual service groupings and automatic groupings that allow analyzing, evaluating, and validating clustering techniques applied to improve service cohesion and fragmentation in SOA-driven software projects. Concerning quantifying the quality of our clustering solutions, we relied on external quality metrics, which measure how well the clustering results match some prior knowledge (manual service groupings by real developers in our case) about the data. Additionally, we have performed improvements in existing clustering techniques of previous work, VizSOC, reaching a 20% gain regarding the metrics mentioned above. Moreover, we added an implementation of the COBWEB clustering algorithm to expand our research, yielding fruitful results.

The problem of migrating monolithic applications to microservices has become popular both in industry and academia, particularly when using automated tools to assist developers in the decomposition. While a variety of tools and techniques have been proposed, deciding which is the most appropriate decomposition for a given monolith is challenging because the selected technique can return alternative decompositions depending on how the parameters of that technique are configured. This issue has not received enough attention in the literature, and therefore, developers have to resort to their intuition or use the default parameters reported by the authors of the technique. To investigate this problem further, in this work we perform a study of the parameters and variability of the MicroMiner approach, assessing its parameter sensitivity when dealing with two monolithic applications from the literature. Based on a systematic, data-driven analysis of the landscape of possible decompositions, our results show that, depending on the monolithic application provided as input, certain parameters of MicroMiner have more or less importance on the characteristics of the generated decompositions. These findings provide initial guidelines for developers to configure MicroMiner, as well as other approaches, in order to obtain microservice decompositions with relatively low variability.

Service-oriented computing (SOC) has been widely used by software industry for building distributed software applications that can be run in heterogeneous environments. It has also been required that these applications should be both high-quality and adaptable to market changes. However, a major problem in this type of applications is its growth; as the size and complexity of applications increase, the probability of duplicity of code increases. This problem could have a negative impact on quality attributes, such as performance, maintenance and evolution, among others. This paper presents a web tool called VizSOC to assist software developers in detecting refactoring opportunities in service-oriented applications. The tool receives WSDL (Web Service Description Language) documents, detects anti-patterns and suggests how to resolve them, and delivers a list of refactoring suggestions to start working on the refactoring process. To visualize the results in an orderly and comprehensible way, we use the Hierarchical Edge Bundles (HEB) visualization technique. The experimentation of the tool has been supported using two real-life case-studies, where we measured the amount of anti-patterns detected and the performance of clustering algorithms by using internal validity criteria. The results indicate that VizSOC is an effective aid to detect refactoring opportunities, and also allows developers to reduce effort along the detection process.