Informatik
Refine
Year of publication
- 2021 (17) (remove)
Document Type
- Conference proceeding (12)
- Journal article (4)
- Book chapter (1)
Has full text
- yes (17) (remove)
Is part of the Bibliography
- yes (17)
Institute
- Informatik (17)
Publisher
- Springer (17) (remove)
Several studies analyzed existing Web APIs against the constraints of REST to estimate the degree of REST compliance among state-of-the-art APIs. These studies revealed that only a small number of Web APIs are truly RESTful. Moreover, identified mismatches between theoretical REST concepts and practical implementations lead us to believe that practitioners perceive many rules and best practices aligned with these REST concepts differently in terms of their importance and impact on software quality. We therefore conducted a Delphi study in which we confronted eight Web API experts from industry with a catalog of 82 REST API design rules. For each rule, we let them rate its importance and software quality impact. As consensus, our experts rated 28 rules with high, 17 with medium, and 37 with low importance. Moreover, they perceived usability, maintainability, and compatibility as the most impacted quality attributes. The detailed analysis revealed that the experts saw rules for reaching Richardson maturity level 2 as critical, while reaching level 3 was less important. As the acquired consensus data may serve as valuable input for designing a tool-supported approach for the automatic quality evaluation of RESTful APIs, we briefly discuss requirements for such an approach and comment on the applicability of the most important rules.
Context: Agile practices as well as UX methods are nowadays well-known and often adopted to develop complex software and products more efficiently and effectively. However, in the so called VUCA environment, which many companies are confronted with, the sole use of UX research is not sufficient to find the best solutions for customers. The implementation of Design Thinking can support this process. But many companies and their product owners don’t know how much resources they should spend for conducting Design Thinking.
Objective: This paper aims at suggesting a supportive tool, the “Discovery Effort Worthiness (DEW) Index”, for product owners and agile teams to determine a suitable amount of effort that should be spent for Design Thinking activities.
Method: A case study was conducted for the development of the DEW index. Design Thinking was introduced into the regular development cycle of an industry Scrum team. With the support of UX and Design Thinking experts, a formula was developed to determine the appropriate effort for Design Thinking.
Results: The developed “Discovery Effort Worthiness Index” provides an easy-to-use tool for companies and their product owners to determine how much effort they should spend on Design Thinking methods to discover and validate requirements. A company can map the corresponding Design Thinking methods to the results of the DEW Index calculation, and product owners can select the appropriate measures from this mapping. Therefore, they can optimize the effort spent for discovery and validation.
The cloud evolved into an attractive execution environment for parallel applications, which make use of compute resources to speed up the computation of large problems in science and industry. Whereas Infrastructure as a Service (IaaS) offerings have been commonly employed, more recently, serverless computing emerged as a novel cloud computing paradigm with the goal of freeing developers from resource management issues. However, as of today, serverless computing platforms are mainly used to process computations triggered by events or user requests that can be executed independently of each other and benefit from on-demand and elastic compute resources as well as per-function billing. In this work, we discuss how to employ serverless computing platforms to operate parallel applications. We specifically focus on the class of parallel task farming applications and introduce a novel approach to free developers from both parallelism and resource management issues. Our approach includes a proactive elasticity controller that adapts the physical parallelism per application run according to user-defined goals. Specifically, we show how to consider a user-defined execution time limit after which the result of the computation needs to be present while minimizing the associated monetary costs. To evaluate our concepts, we present a prototypical elastic parallel system architecture for self-tuning serverless task farming and implement two applications based on our framework. Moreover, we report on performance measurements for both applications as well as the prediction accuracy of the proposed proactive elasticity control mechanism and discuss our key findings.
Lehre und Lernen unterliegt einem stetigen Wandel, wobei Interaktion als ein zentrales Element der Motivationssteigerung im Lernkontext angesehen wird. Der vorliegende Beitrag zeigt verschiedene Ansätze zur Gestaltung von interaktivem und kollaborativem Lehren und Lernen in einem virtuellen Klassenzimmer auf und stellt ein Beispiel für die Umsetzung und den Einsatz eines solchen Systems vor. Die Mehrwerte und Erfolgsfaktoren, die sich beim Einsatz virtueller Klassenzimmer und deren Gestaltung in Form einer interaktiven blended-learning Umgebung ergeben, werden dargestellt und diskutiert. Mit dem System Accelerator wird eine CSILT (Computer Supported Interactive Learning and Teaching)-Umgebung vorgestellt, in der diese Faktoren zum Einsatz kommen.
Context
Microservices 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.
Objective
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.
Method
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.
Results
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.
Conclusions
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.
Identifikation von Schlaf- und Wachzuständen durch die Auswertung von Atem- und Bewegungssignalen
(2021)
Unternehmen wenden insbesondere bei IT-nahen Projekten seit einigen Jahren auch im Controlling verstärkt ein agiles Vorgehen an. Erfahrungen zeigen jedoch, dass dies nicht bei allen Projekten in jedem Unternehmen funktioniert. Hybride Ansätze, die agile mit klassischen Projekt-Management-Methoden verbinden, bieten eine Lösung.
Context: Nowadays, companies are challenged by increasing market dynamics, rapid changes and disruptive participants entering the market. To survive in such an environment, companies must be able to quickly discover product ideas that meet the needs of both customers and the company and deliver these products to customers. Dual-track agile is a new type of agile development that combines product discovery and delivery activities in parallel, iterative, and cyclical ways. At present, many companies have difficulties in finding and establishing suitable approaches for implementing dual-track agile in their business context.
Objective: In order to gain a better understanding of how product discovery and product delivery can interact with each other and how this interaction can be implemented in practice, this paper aims to identify suitable approaches to dual-track agile.
Method: We conducted a grey literature review (GLR) according to the guidelines to Garousi et al.
Results: Several approaches that support the integration of product discovery with product delivery were identified. This paper presents a selection of these approaches, i.e., the Discovery-Delivery Cycle model, Now-Next-Later Product Roadmaps, Lean Sprints, Product Kata, and Dual-Track Scrum. The approaches differ in their granularity but are similar in their underlying rationales. All approaches aim to ensure that only validated ideas turn into products and thus promise to lead to products that are better received by their users.
The Internet of Things (IoT) is coined by many different standards, protocols, and data formats that are often not compatible to each other. Thus, the integration of different heterogeneous (IoT) components into a uniform IoT setup can be a time-consuming manual task. This lacking interoperability between IoT components has been addressed with different approaches in the past. However, only very few of these approaches rely on Machine Learning techniques. In this work, we present a new way towards IoT interoperability based on Deep Reinforcement Learning (DRL). In detail, we demonstrate that DRL algorithms, which use network architectures inspired by Natural Language Processing (NLP), can be applied to learn to control an environment by merely taking raw JSON or XML structures, which reflect the current state of the environment, as input. Applied to IoT setups, where the current state of a component is often reflected by features embedded into JSON or XML structures and exchanged via messages, our NLP DRL approach eliminates the need for feature engineering and manually written code for pre-processing of data, feature extraction, and decision making.
Platforms and their surrounding ecosystems are becoming increasingly important components of many companies' strategies. Artificial Intelligence, in particular, has created new opportunities to create and develop ecosystems around the platform. However, there is not yet a methodology to systematically develop these new opportunities for enterprise development strategy. Therefore, this paper aims to lay a foundation for the conceptualization of Artificial Intelligence-based service ecosystems exploiting a Service-Dominant Logic. The basis for conceptualization is the study of value creation and particularly effective network effects. This research investigates the fundamental idea of extending specific digital concepts considering the influence of Artificial Intelligence on the design of intelligent services, along with their architecture of digital platforms and ecosystems, to enable a smooth evolutionary path and adaptability for human-centric collaborative systems and services. The paper explores an extended digital enterprise conceptual model through a combined, iterative, and permanent task of co-creating value between humans and intelligent systems as part of a new idea of cognitively adapted intelligent services.
The current advancement of Artificial Intelligence (AI) combined with other digitalization efforts significantly impacts service ecosystems. Artificial intelligence has a substantial impact on new opportunities for the co-creation of value and the development of intelligent service ecosystems. Motivated by experiences and observations from digitalization projects, this paper presents new methodological perspectives and experiences from academia and practice on architecting intelligent service ecosystems and explores the impact of artificial intelligence through real cases supporting an ongoing validation. Digital enterprise architecture models serve as an integral representation of business, information, and technological perspectives of intelligent service-based enterprise systems to support management and development. This paper focuses on architectural models for intelligent service ecosystems, showing the fundamental business mechanism of AI-based value co-creation, the corresponding digital architecture, and management models. The focus of this paper presents the key architectural model perspectives for the development of intelligent service ecosystems.
Context: Many companies are facing an increasingly dynamic and uncertain market environment, making traditional product roadmapping practices no longer sufficiently applicable. As a result, many companies need to adapt their product roadmapping practices for continuing to operate successfully in today’s dynamic market environment. However, transforming product roadmapping practices is a difficult process for organizations. Existing literature offers little help on how to accomplish such a process.
Objective: The objective of this paper is to present a product roadmap transformation approach for organizations to help them identify appropriate improvement actions for their roadmapping practices using an analysis of their current practices.
Method: Based on an existing assessment procedure for evaluating product roadmapping practices, the first version of a product roadmap transformation approach was developed in workshops with company experts. The approach was then given to eleven practitioners and their perceptions of the approach were gathered through interviews.
Results: The result of the study is a transformation approach consisting of a process describing what steps are necessary to adapt the currently applied product roadmapping practice to a dynamic and uncertain market environment. It also includes recommendations on how to select areas for improvement and two empirically based mapping tables. The interviews with the practitioners revealed that the product roadmap transformation approach was perceived as comprehensible, useful, and applicable. Nevertheless, we identified potential for improvements, such as a clearer presentation of some processes and the need for more improvement options in the mapping tables. In addition, minor usability issues were identified.
A hybrid deep registration of MR scans to interventional ultrasound for neurosurgical guidance
(2021)
Despite the recent advances in image-guided neurosurgery, reliable and accurate estimation of the brain shift still remains one of the key challenges. In this paper, we propose an automated multimodal deformable registration method using hybrid learning-based and classical approaches to improve neurosurgical procedures. Initially, the moving and fixed images are aligned using classical affine transformation (MINC toolkit), and then the result is provided to the convolutional neural network, which predicts the deformation field using backpropagation. Subsequently, the moving image is transformed using the resultant deformation into a moved image. Our model was evaluated on two publicly available datasets: the retrospective evaluation of cerebral tumors (RESECT) and brain images of tumors for evaluation (BITE). The mean target registration errors have been reduced from 5.35 ± 4.29 to 0.99 ± 0.22 mm in the RESECT and from 4.18 ± 1.91 to 1.68 ± 0.65 mm in the BITE. Experimental results showed that our method improved the state-of-the-art in terms of both accuracy and runtime speed (170 ms on average). Hence, the proposed method provides a fast runtime for 3D MRI to intra-operative US pair in a GPU-based implementation, which shows a promise for its applicability in assisting the neurosurgical procedures compensating for brain shift.
In recent years, artificial intelligence (AI) has increasingly become a relevant technology for many companies. While there are a number of studies that highlight challenges and success factors in the adoption of AI, there is a lack of guidance for firms on how to approach the topic in a holistic and strategic way. The aim of this study is therefore to develop a conceptual framework for corporate AI strategy. To address this aim, a systematic literature review of a wide spectrum of AI-related research is conducted, and the results are analyzed based on an inductive coding approach. An important conclusion is that companies should consider diverse aspects when formulating an AI strategy, ranging from technological questions to corporate culture and human resources. This study contributes to knowledge by proposing a novel, comprehensive framework to foster the understanding of crucial aspects that need to be considered when using the emerging technology of AI in a corporate context.
Context:
Test-driven development (TDD) is an agile software development approach that has been widely claimed to improve software quality. However, the extent to which TDD improves quality appears to be largely dependent upon the characteristics of the study in which it is evaluated (e.g., the research method, participant type, programming environment, etc.). The particularities of each study make the aggregation of results untenable.
Objectives:
The goal of this paper is to: increase the accuracy and generalizability of the results achieved in isolated experiments on TDD, provide joint conclusions on the performance of TDD across different industrial and academic settings, and assess the extent to which the characteristics of the experiments affect the quality-related performance of TDD.
Method:
We conduct a family of 12 experiments on TDD in academia and industry. We aggregate their results by means of meta-analysis. We perform exploratory analyses to identify variables impacting the quality-related performance of TDD.
Results:
TDD novices achieve a slightly higher code quality with iterative test-last development (i.e., ITL, the reverse approach of TDD) than with TDD. The task being developed largely determines quality. The programming environment, the order in which TDD and ITL are applied, or the learning effects from one development approach to another do not appear to affect quality. The quality-related performance of professionals using TDD drops more than for students. We hypothesize that this may be due to their being more resistant to change and potentially less motivated than students.
Conclusion:
Previous studies seem to provide conflicting results on TDD performance (i.e., positive vs. negative, respectively). We hypothesize that these conflicting results may be due to different study durations, experiment participants being unfamiliar with the TDD process, or case studies comparing the performance achieved by TDD vs. the control approach (e.g., the waterfall model), each applied to develop a different system. Further experiments with TDD experts are needed to validate these hypotheses.
The main aim of presented in this manuscript research is to compare the results of objective and subjective measurement of sleep quality for older adults (65+) in the home environment. A total amount of 73 nights was evaluated in this study. Placing under the mattress device was used to obtain objective measurement data, and a common question on perceived sleep quality was asked to collect the subjective sleep quality level. The achieved results confirm the correlation between objective and subjective measurement of sleep quality with the average standard deviation equal to 2 of 10 possible quality points.
The digitization of factories will be a significant issue for the 2020s. New scenarios are emerging to increase the efficiency of production lines inside the factory, based on a new generation of robots’ collaborative functions. Manufacturers are moving towards data-driven ecosystems by leveraging product lifecycle data from connected goods. Energy-efficient communication schemes, as well as scalable data analytics, will support these various data collection scenarios. With augmented reality, new remote services are emerging that facilitate the efficient sharing of knowledge in the factory. Future communication solutions should generally ensure connectivity between the various production sites spread worldwide and new players in the value chain (e.g., suppliers, logistics) transparent, real-time, and secure. Industry 4.0 brings more intelligence and flexibility to production. Resulting in more lightweight equipment and, thus, offering better ergonomics. 5G will guarantee real-time transmissions with latencies of less than 1 ms. This will provide manufacturers with new possibilities to collect data and trigger actions automatically.