Context: The popularity of microservices architecture has grown due to its ability to address monolithic architecture issues, such as limited scalability, hard maintenance, and technological dependence. Nonetheless, the migration of monolith systems to microservices is complex. Therefore, methodologies and techniques are needed to facilitate migration and support practitioners and software architects. Objective: The objective of this study is to investigate cases of application migration, microservices identification techniques, tools used during migration, factors that promote migration, as well as issues and benefits of the migration. Method: We have conducted this SMS following the guidelines established by Kitchenham and Petersen. The research objective was defined using part of the Goal-Question-Metric model and the Population, Intervention, and Outcome criteria. From 1546 studies that were retrieved from the search execution , 114 were selected and analyzed to answer the research questions. Results: This SMS contributes with (i) a migration process proposal based on migration cases, (ii) a characterization of migration techniques based on different criteria, (iii) an analysis of tools to support migration, (iv) the identification of migration drivers, and (v) an exploration of migration issues as well as benefits. Conclusion: This SMS sheds light on the complexity and variability of migrating monolithic systems to microservices, as well as the limited number of migration tools. While scalability and maintenance drive migration, few studies assess them. Key challenges include microservices communication and database migration , with most research focusing primarily on monolith decomposition. Despite these difficulties, migration offers benefits, particularly in scalability and maintainability .

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.

A demonstration of the application and contribution of Test-Driven Development (TDD) and Behavior-Driven Development (BDD) in the student learning experience in the context of a Software Engineering course. Background: Software testing is an activity for ensuring software quality. Although teaching testing rigorously to students is a priority in academia, undergraduate students often encounter difficulties performing testing tasks effectively. Intended Outcomes: To increase satisfaction rate and course grades, and reduce delivery time. Application Design: We experimented with the Software Engineering Laboratory (LES) course of a Private University in the Bachelor of Computer Science and Information Systems courses. This experiment corroborated the learning difficulties of students. Collected data were assessed both quantitatively and qualitatively. Findings: Backed up with statistical tests, the results showed a reduction in student absences, higher student satisfaction rate, and higher grades in the courses. Furthermore, our approach allowed students to deliver a product in a short period, representing a possibility of adoption of BDD due to their successful learning experience. Finally, we aim to foster a discussion of appropriate teaching methods of software testing.

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.

Service-Oriented Architecture (SOA) has gained considerable popularity for the development of distributed enterprise-wide applications within the software industry. The SOA paradigm promotes the reusability and integrability of software in heterogeneous environments by means of open standards. Most software companies capitalize on SOA by discovering and composing services already accessible over the Internet, whereas other organizations need internal control of applications and develop new services with quality-attribute properties tailored to their particular environment. Therefore, based on architectural and business requirements, developers can elaborate different alternatives within a SOA framework to design their software applications. Each of these alternatives will imply trade-offs among quality attributes, such as performance, dependability and availability, among others. In this context, Artificial Intelligence (AI) can assist developers in dealing with service-oriented design with the positive impact on scalability and management of generic quality attributes. In this paper, we offer a detailed, conceptualized and synthesized analysis of AI research works that have aimed at discovering, composing, or developing services. We also identify open research issues and challenges in the aforementioned research areas. The results of the characterization of 69 contemporary approaches and potential research directions for the areas are also shown. It is concluded that AI has aimed at exploiting the semantic resources and achieving quality-attribute properties so as to produce flexible and adaptive-to-change service discovery, composition, and development.