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Enterprise Architectures (EA) consists of many architecture elements, which stand in manifold relationships to each other. Therefore Architecture Analysis is important and very difficult for stakeholders. Due changing an architecture element has impacts on other elements different stakeholders are involved. In practice EAs are often analyzed using visualizations. This article aims at contributing to the field of visual analytics in EAM by analyzing how state of-the-art software platforms in EAM support stakeholders with respect to providing and visualizing the “right” information for decision-making tasks. We investigate the collaborative decision-making process in an experiment with master students using professional EAM tools by developing a research study and accomplishing them in a master’s level class with students.
Current approaches for enterprise architecture lack analytical instruments for cyclic evaluations of business and system architectures in real business enterprise system environments. This impedes the broad use of enterprise architecture methodologies. Furthermore, the permanent evolution of systems desynchronizes quickly model representation and reality. Therefore we are introducing an approach for complementing the existing top-down approach for the creation of enterprise architecture with a bottom approach. Enterprise Architecture Analytics uses the architectural information contained in many infrastructures to provide architectural information. By applying Big Data technologies it is possible to exploit this information and to create architectural information. That means, Enterprise Architectures may be discovered, analyzed and optimized using analytics. The increased availability of architectural data also improves the possibilities to verify the compliance of Enterprise Architectures. Architectural decisions are linked to clustered architecture artifacts and categories according to a holistic EAM Reference Architecture with specific architecture metamodels. A special suited EAM Maturity Framework provides the base for systematic and analytics supported assessments of architecture capabilities.
A configuration-management-database driven approach for fabric-process specification and automation
(2014)
In this paper we describe an approach that integrates a Configuration- Management-Database into fabric-process specification and automation in order to consider different conditions regarding to cloud-services. By implementing our approach, the complexity of fabric processes gets reduced. We developed a prototype by using formal prototyping principles as research methods and integrated the Configuration-Management-Database Command into the Workflow- Management-System Activiti. We used this prototype to evaluate our approach. We implemented three different fabric-processes and show that by using our approach the complexity of these three fabric-processes gets reduced.
SmartLife ecosystems are emerging as intelligent user-centered systems that will shape future trends in technology and communication. Biological metaphors of living adaptable ecosystems provide the logical foundation for self-optimizing and self-healing run-time environments for intelligent adaptable business services and related information systems with service-oriented enterprise architectures. The present research in progress work investigates mechanisms for adaptable enterprise architectures for the development of service-oriented ecosystems with integrated technologies like Semantic Technologies, Web Services, Cloud Computing and Big Data Management. With a large and diverse set of ecosystem services with different owners, our scenario of service-based SmartLife ecosystems can pose challenges in their development, and more importantly, for maintenance and software evolution. Our research explores the use of knowledge modeling using ontologies and flexible metamodels for adaptable enterprise architectures to support program comprehension for software engineers during maintenance and evolution tasks of service-based applications. Our previous reference enterprise architecture model ESARC -- Enterprise Services Architecture Reference Cube -- and the Open Group SOA Ontology was extended to support agile semantic analysis, program comprehension and software evolution for a SmartLife applications scenario. The Semantic Browser is a semantic search tool that was developed to provide knowledge-enhanced investigation capabilities for service-oriented applications and their architectures.
The advent of chatbots in customer service solutions received increasing attention by research and practice throughout the last years. However, the relevant dimensions and features for service quality and service performance for chatbots remain quite unclear. Therefore, this research develops and tests a conceptual model for customer service quality and customer service performance in the context of chatbots. Additionally, the impact of the developed service dimensions on different customer relationship metrics is measured across different service channels (hotline versus chatbots). Findings of six independent studies indicate a strong main effect of the conceptualized service dimensions on customer satisfaction, service costs, intention to service reusage, word-of-mouth, and customer loyalty. However, different service dimensions are relevant for chatbots compared to a traditional service hotline.
Software evolvability is an important quality attribute, yet one difficult to grasp. A certain base level of it is allegedly provided by service- and microservice-based systems, but many software professionals lack systematic understanding of the reasons and preconditions for this. We address this issue via the proxy of architectural modifiability tactics. By qualitatively mapping principles and patterns of Service Oriented Architecture (SOA) and microservices onto tactics and analyzing the results, we cannot only generate insights into service-oriented evolution qualities, but can also provide a modifiability comparison of the two popular service-based architectural styles. The results suggest that both SOA and microservices possess several inherent qualities beneficial for software evolution. While both focus strongly on loose coupling and encapsulation, there are also differences in the way they strive for modifiability (e.g. governance vs. evolutionary design). To leverage the insights of this research, however, it is necessary to find practical ways to incorporate the results as guidance into the software development process.
Background: Design patterns are supposed to improve various quality attributes of software systems. However, there is controversial quantitative evidence of this impact. Especially for younger paradigms such as service- and microservice-based systems, there is a lack of empirical studies.
Objective: In this study, we focused on the effect of four service-based patterns - namely process abstraction, service façade, decomposed capability, and event-driven messaging - on the evolvability of a system from the viewpoint of inexperienced developers.
Method: We conducted a controlled experiment with Bachelor students (N = 69). Two functionally equivalent versions of a service-based web shop - one with patterns (treatment group), one without (control group) - had to be changed and extended in three tasks. We measured evolvability by the effectiveness and efficiency of the participants in these tasks. Additionally, we compared both system versions with nine structural maintainability metrics for size, granularity, complexity, cohesion, and coupling.
Results: Both experiment groups were able to complete a similar number of tasks within the allowed 90 min. Median effectiveness was 1/3. Mean efficiency was 12% higher in the treatment group, but this difference was not statistically significant. Only for the third task, we found statistical support for accepting the alternative hypothesis that the pattern version led to higher efficiency. In the metric analysis, the pattern version had worse measurements for size and granularity while simultaneously having slightly better values for coupling metrics. Complexity and cohesion were not impacted.
Interpretation: For the experiment, our analysis suggests that the difference in efficiency is stronger with more experienced participants and increased from task to task. With respect to the metrics, the patterns introduce additional volume in the system, but also seem to decrease coupling in some areas.
Conclusions: Overall, there was no clear evidence for a decisive positive effect of using service-based patterns, neither for the student experiment nor for the metric analysis. This effect might only be visible in an experiment setting with higher initial effort to understand the system or with more experienced developers.
While the concepts of object-oriented antipatterns and code smells are prevalent in scientific literature and have been popularized by tools like SonarQube, the research field for service-based antipatterns and bad smells is not as cohesive and organized. The description of these antipatterns is distributed across several publications with no holistic schema or taxonomy. Furthermore, there is currently little synergy between documented antipatterns for the architectural styles SOA and Microservices, even though several antipatterns may hold value for both. We therefore conducted a Systematic Literature Review (SLR) that identified 14 primary studies. 36 service-based antipatterns were extracted from these studies and documented with a holistic data model. We also categorized the antipatterns with a taxonomy and implemented relationships between them. Lastly, we developed a web application for convenient browsing and implemented a GitHub-based repository and workflow for the collaborative evolution of the collection. Researchers and practitioners can use the repository as a reference, for training and education, or for quality assurance.
Microservices are a topic driven mainly by practitioners and academia is only starting to investigate them. Hence, there is no clear picture of the usage of Microservices in practice. In this paper, we contribute a qualitative study with insights into industry adoption and implementation of Microservices. Contrary to existing quantitative studies, we conducted interviews to gain a more in-depth understanding of the current state of practice. During 17 interviews with software professionals from 10 companies, we analyzed 14 service-based systems. The interviews focused on applied technologies, Microservices characteristics, and the perceived influence on software quality. We found that companies generally rely on well established technologies for service implementation, communication, and deployment. Most systems, however, did not exhibit a high degree of technological diversity as commonly expected with Microservices. Decentralization and product character were different for systems built for external customers. Applied DevOps practices and automation were still on a mediocre level and only very few companies strictly followed the you build it, you run it principle. The impact of Microservices on software quality was mainly rated as positive. While maintainability received the most positive mentions, some major issues were associated with security. We present a description of each case and summarize the most important findings of companies across different domains and sizes. Researchers may build upon our findings and take them into account when designing industry-focused methods.
While several service-based maintainability metrics have been proposed in the scientific literature, reliable approaches to automatically collect these metrics are lacking. Since static analysis is complicated for decentralized and technologically diverse microservice-based systems, we propose a dynamic approach to calculate such metrics from runtime data via distributed tracing. The approach focuses on simplicity, extensibility, and broad applicability. As a first prototype, we implemented a Java application with a Zipkin integrator, 23 different metrics, and five export formats. We demonstrated the feasibility of the approach by analyzing the runtime data of an example microservice based system. During an exploratory study with six participants, 14 of the 18 services were invoked via the system’s web interface. For these services, all metrics were calculated correctly from the generated traces.