05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Architectural refactoring to microservices : a quality-driven methodology for modernizing monolithic applications(2024) Fritzsch, Jonas; Wagner, Stefan (Prof. Dr.)Context and Problem: The microservices architectural style has revolutionized the way modern software systems are developed and operated, and has become the de facto standard for cloud-based applications. However, existing systems are often designed as monoliths, which are associated with inflexible processes, long release cycles, and an architecture incapable of leveraging the advantages of cloud environments. The adoption of microservices would require an architectural refactoring, entailing redevelopments of parts or even the entire application. Often associated with extensive manual effort, the targeted, quality-oriented, and semi-automated decomposition into a set of self-contained services remains problematic. Software architects look for resource-efficient ways to provide predictable results and guidance in this highly individual process. Objective: To systematically guide software architects and developers in modernizing their software systems, we seek to provide a holistic methodology for systematic and quality-driven migrations towards microservices. As part of it, we search solutions for the targeted and automated decomposition into services, and ways to support a quality-oriented design based on established patterns and best practices. Our work aims to provide industry-relevant methods that address the gap between academia and practice by facilitating the transfer of knowledge. Methods: In an overarching design science research process, we create a migration methodology that we implement as a web-based application. For analysis and evaluation, we apply established methods in empirical software engineering, such as case study research, surveys, and semi-structured interviews with experts. Our secondary research to summarize the current state of scientific advances relies on consecutive literature searches and rapid reviews, a lightweight method derived from systematic reviews. Contributions: Based on two primary empirical interview studies with 25 software professionals, we collected evidence on the intentions, strategies, and challenges of migrating monolithic applications to microservices, complemented by requirements for tool support and automation. Over four iterations, we reviewed 110 scientific publications on approaches for architectural refactoring and migration to microservices. To guide architects and developers in a migration process, we conceptualized a framework, along with a dedicated quality model that reflects a quality-driven migration process. Based on latest technologies and a modern user interface design, we realized our concept as a web-based application in an agile development process with early involvement of potential users. In a multi-faceted evaluation, we examined its ability to provide actionable guidance for practitioners. To this end, we conducted three surveys and one interview study with a total of 26 participants, complemented by two longitudinal case studies in an industrial context. Conclusion: We propose a holistic methodology for modernizing monolithic applications to microservices that comprises a framework and a dedicated quality model. Our contributions support architects in making informed decisions about microservices adoption, and furthermore guide them through a systematic transformation process. The evaluations showed an overall positive result in terms of effectiveness, usefulness, and usability, while both case studies demonstrated a successful application in an industrial environment. By sharing important study artifacts, we support researchers developing industry-focused methods, who can profit from our insights and experiences. Moreover, we regard our design science approach to leveraging academic research by practice as transferable to other scientific disciplines.Item Open Access Adopting microservices and DevOps in the cyber‐physical systems domain : a rapid review and case study(2022) Fritzsch, Jonas; Bogner, Justus; Haug, Markus; Franco da Silva, Ana Cristina; Rubner, Carolin; Saft, Matthias; Sauer, Horst; Wagner, StefanThe domain of cyber‐physical systems (CPS) has recently seen strong growth, for example, due to the rise of the Internet of Things (IoT) in industrial domains, commonly referred to as “Industry 4.0.” However, CPS challenges like the strong hardware focus can impact modern software development practices, especially in the context of modernizing legacy systems. While microservices and DevOps have been widely studied for enterprise applications, there is insufficient coverage for the CPS domain. Our goal is therefore to analyze the peculiarities of such systems regarding challenges and practices for using and migrating towards microservices and DevOps. We conducted a rapid review based on 146 scientific papers, and subsequently validated our findings in an interview‐based case study with nine CPS professionals in different business units at Siemens AG. The combined results picture the specifics of microservices and DevOps in the CPS domain. While several differences were revealed that may require adapted methods, many challenges and practices are shared with typical enterprise applications. Our study supports CPS researchers and practitioners with a summary of challenges, practices to address them, and research opportunities.Item Open Access Industry practices and challenges for the evolvability assurance of microservices : an interview study and systematic grey literature review(2021) Bogner, Justus; Fritzsch, Jonas; Wagner, Stefan; Zimmermann, AlfredMicroservices as a lightweight and decentralized architectural style with fine-grained services promise several beneficial characteristics for sustainable long-term software evolution. Success stories from early adopters like Netflix, Amazon, or Spotify have demonstrated that it is possible to achieve a high degree of flexibility and evolvability with these systems. However, the described advantageous characteristics offer no concrete guidance and little is known about evolvability assurance processes for microservices in industry as well as challenges in this area. Insights into the current state of practice are a very important prerequisite for relevant research in this field. We therefore wanted to explore how practitioners structure the evolvability assurance processes for microservices, what tools, metrics, and patterns they use, and what challenges they perceive for the evolvability of their systems. We first conducted 17 semi-structured interviews and discussed 14 different microservice-based systems and their assurance processes with software professionals from 10 companies. Afterwards, we performed a systematic grey literature review (GLR) and used the created interview coding system to analyze 295 practitioner online resources. The combined analysis revealed the importance of finding a sensible balance between decentralization and standardization. Guidelines like architectural principles were seen as valuable to ensure a base consistency for evolvability and specialized test automation was a prevalent theme. Source code quality was the primary target for the usage of tools and metrics for our interview participants, while testing tools and productivity metrics were the focus of our GLR resources. In both studies, practitioners did not mention architectural or service-oriented tools and metrics, even though the most crucial challenges like Service Cutting or Microservices Integration were of an architectural nature. Practitioners relied on guidelines, standardization, or patterns like Event-Driven Messaging to partially address some reported evolvability challenges. However, specialized techniques, tools, and metrics are needed to support industry with the continuous evaluation of service granularity and dependencies. Future microservices research in the areas of maintenance, evolution, and technical debt should take our findings and the reported industry sentiments into account.Item Open Access How do ML practitioners perceive explainability? : an interview study of practices and challenges(2024) Habiba, Umm-e-; Habib, Mohammad Kasra; Bogner, Justus; Fritzsch, Jonas; Wagner, StefanExplainable artificial intelligence (XAI) is a field of study that focuses on the development process of AI-based systems while making their decision-making processes understandable and transparent for users. Research already identified explainability as an emerging requirement for AI-based systems that use machine learning (ML) techniques. However, there is a notable absence of studies investigating how ML practitioners perceive the concept of explainability, the challenges they encounter, and the potential trade-offs with other quality attributes. In this study, we want to discover how practitioners define explainability for AI-based systems and what challenges they encounter in making them explainable. Furthermore, we explore how explainability interacts with other quality attributes. To this end, we conducted semi-structured interviews with 14 ML practitioners from 11 companies. Our study reveals diverse viewpoints on explainability and applied practices. Results suggest that the importance of explainability lies in enhancing transparency, refining models, and mitigating bias. Methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanation (LIME) are frequently used by ML practitioners to understand how models work, while tailored approaches are typically adopted to meet the specific requirements of stakeholders. Moreover, we have discerned emerging challenges in eight categories. Issues such as effective communication with non-technical stakeholders and the absence of standardized approaches are frequently stated as recurring hurdles. We contextualize these findings in terms of requirements engineering and conclude that industry currently lacks a standardized framework to address arising explainability needs.