Mobile shopping apps have been using Augmented Reality (AR) in the last years to place their products in the environment of the customer. While this is possible with atomic 3D objects, there is is still a lack in the runtime configuration of 3D object compositions based on user needs and environmental constraints. For this, we previously developed an approach for model-based AR-assisted product configuration based on the concept of Dynamic Software Product Lines. In this demonstration paper, we present the corresponding tool support ProConAR in the form of a Product Modeler and a Product Configurator. While the Product Modeler is an Angular web app that splits products (e.g. table) up into atomic parts (e.g. tabletop, table legs, funnier) and saves it within a configuration model, the Product Configurator is an Android client that uses the configuration model to place different product configurations within the environment of the customer. We show technical details of our ready to use tool-chain ProConAR by describing its implementation and usage as well as pointing out future research directions.

The continuous innovation of its business models is an important task for a company to stay competitive. During this process, the company has to validate various hypotheses about its business models by adapting to uncertain and changing customer needs effectively and efficiently. This adaptation, in turn, can be supported by the concept of Software Product Lines (SPLs). SPLs reduce the time to market by deriving products for customers with changing requirements using a common set of features, structured as a feature model. Analogously, we support the process of business model adaptation by applying the engineering process of SPLs to the structure of the Business Model Canvas (BMC). We call this concept a Business Model Decision Line (BMDL). The BMDL matches business domain knowledge in the form of a feature model with customer needs to derive hypotheses about the business model together with experiments for validation. Our approach is effective by providing a comprehensive overview of possible business model adaptations and efficient by reusing experiments for different hypotheses. We implement our approach in a tool and illustrate the usefulness with an example of developing business models for a mobile application.

Augmented Reality (AR) has recently found high attention in mobile shopping apps such as in domains like furniture or decoration. Here, the developers of the apps focus on the positioning of atomic 3D objects in the physical environment. With this focus, they neglect the configuration of multi-faceted 3D object composition according to the user needs and environmental constraints. To tackle these challenges, we present a model-based approach to support AR-assisted product con-figuration based on the concept of Dynamic Software Product Lines. Our approach splits products (e.g. table) into parts (e.g. tabletop, ta-ble legs, funnier) with their 3D objects and additional information (e.g. name, price). The possible products, which can be configured out of these parts, are stored in a feature model. At runtime, this feature model can be used to configure 3D object compositions out of the product parts and adapt to user needs and environmental constraints. The benefits of this approach are demonstrated by a case study of configuring modular kitchens with the help of a prototypical mobile-based implementation.

To build successful products, the developers have to adapt their product features and business models to uncertain customer needs. This adaptation is part of the research discipline of Hypotheses Engineering (HE) where customer needs can be seen as hypotheses that need to be tested iteratively by conducting experiments together with the customer. So far, modeling support and associated traceability of this iterative process are missing. Both, in turn, are important to document the adaptation to the customer needs and identify experiments that provide most evidence to the customer needs. To target this issue, we introduce a model-based HE approach with a twofold contribution: First, we develop a modeling language that models hypotheses and experiments as interrelated hierarchies together with a mapping between them. While the hypotheses are labeled with a score level of their current evidence, the experiments are labeled with a score level of maximum evidence that can be achieved during conduction. Second, we provide an iterative process to determine experiments that offer the most evidence improvement to the modeled hypotheses. We illustrate the usefulness of the approach with an example of testing the business model of a mobile application.

Mobile app stores like Apple's AppStore or Google's PlayStore are highly competitive markets for third-party developers wanting to develop successful applications. During the development process, many developers focus on the multitude of product functions but neglect the business model as an equally important part. As a result, developers often fail to meet customer needs, leading to unnecessary development costs and poor market penetration. This, in turn, raises the question of how we intertwine the business model and product functions during the development process to ensure a better alignment between the two. In this paper, we show this intertwined development by adapting the concept of Twin Peaks to the business model and product functions. Based on feature modeling as an abstraction layer, we introduce the concept of a Business Model Decision Line (BMDL) to structure the business model decisions and their relation to product functions structured in a Software Product Line (SPL). The basis of our feature models is the analysis of top listed applications in the app stores of Apple and Google. To create and modify both models, we provide an incremental feature structuring and iterative feature selection process. This combination of abstraction layer and development process supports third-party developers to build successful applications both from a business and a product perspective.

In the last years, store-oriented software ecosystems are gaining more and more attention from a business perspective. In these ecosystems, third-party developers upload extensions to a store which can be downloaded by end users. While the functional scope of such ecosystems is relatively similar, the underlying business models differ greatly in and between their different product domains (e.g. Mobile Phone, Smart TV). This variability, in turn, makes it challenging for store providers to find a business model that fits their own needs. To handle this variability, we introduce the Business Variability Model (BVM) for modeling business model decisions. The basis of these decisions is the analysis of 60 store-oriented software ecosystems in eight different product domains. We map their business model decisions to the Business Model Canvas, condense them to a variability model and discuss particular variants and their dependencies. Our work provides store providers a new approach for modeling business model decisions together with insights of existing business models. This, in turn, supports them in creating new and improving existing business models.

Self-stabilizing overlay networks have the advantage of being able to recover from illegal states and faults. However, the majority of these networks cannot give any guarantees on their functionality while the recovery process is going on. We are especially interested in searchability, i.e., the functionality that search messages for a specific node are answered successfully if a node exists in the network. In this paper we investigate overlay networks that ensure the maintenance of monotonic searchability while the self-stabilization is going on. More precisely, once a search message from node u to another node v is successfully delivered, all future search messages from u to v succeed as well. We extend the existing research by focusing on skip graphs and present a solution for two scenarios: (i) the goal topology is a super graph of the perfect skip graph and (ii) the goal topology is exactly the perfect skip graph.

Today, model-driven approaches are a cornerstone in modern software development. The Eclipse Modeling Framework (EMF) is highly adopted in practice and generates Java code from platform-independent models with embedded Object Constraint Language (OCL) expressions. However, applications that target multiple platforms like Android, iOS, Windows, web browsers usually need to be implemented in different programming languages. Feature-complete Ecore and OCL runtime APIs are not available for all these platforms, such that their functionality has to be re-implemented. In this paper, we present CrossEcore: A multi-platform enabled modeling framework that generates C#, Swift, TypeScript, and JavaScript code from Ecore models with embedded OCL. An OCL compiler translates OCL expressions into expressions of the target language. The Ecore and OCL API can be consistently used across platforms, which facilitates application portability. CrossEcore is also extendible and can be easily adopted for new programming languages.

Nowadays companies like Apple create ecosystems of third- party providers and users around their software platforms. Often online stores like Apple App Store are created to directly market third-party solutions. We call such ecosystems store-oriented software ecosystems. While the architecture of these ecosystems is mainly derived from busi- ness decisions of their owners, ecosystems with greatly different archi- tectural designs have been created. This diversity makes it challenging for future ecosystem providers to understand which architectural design is suitable to fulfill certain business decisions. In turn, opening a plat- form becomes risky while endangering intellectual property or scarifying quality of services. In this paper, we identify three main design options of store-oriented software ecosystems by classifying existing ecosystems based on similarities in their business decisions. We elaborate on the design options, discuss their main contributions, and provide exemplary ecosystems. Our work provides aspiring ecosystem providers with the reusable knowledge of existing ecosystems and helps them to take more informed architectural decisions and reduce risks in future.

Existing test cases represent important assets, which are worth reusing in software migration projects. The benefit is twofold, reuse of relevant information as well cost saving by avoiding design of new test cases. As test cases are implemented in the same or a compatible technology as the system they are testing, they have to somehow follow the system migration, i.e., they should be co-migrated. Due to the size of the test case set, and often missing conformity in the structure of the test cases, migration of test cases is a quite challenging task. As model-driven engineering has been established to manage those complex tasks, we apply it in the test case domain. In this paper, we propose a generic migration method based on model-driven reengineering techniques. Our method which involves reverse engineering, restructuring, and forward engineering is applied in an industrial case study where appropriate tooling was developed as well.

In this paper we present two major results: First, we introduce the first self-stabilizing version of a supervised overlay network (as introduced in~\cite{DBLP:conf/ispan/KothapalliS05}) by presenting a self-stabilizing supervised skip ring. Secondly, we show how to use the self-stabilizing supervised skip ring to construct an efficient self-stabilizing publish-subscribe system. That is, in addition to stabilizing the overlay network, every subscriber of a topic will eventually know all of the publications that have been issued so far for that topic. The communication work needed to processes a subscribe or unsubscribe operation is just a constant in a legitimate state, and the communication work of checking whether the system is still in a legitimate state is just a constant on expectation for the supervisor as well as any process in the system.

Ein wichtiges Element der Digitalen Transformation ist die Digitalisierung der Prozesse in Unternehmen. Eine Herausforderung besteht hierbei in der systematischen Erkennung von Digitalisierungspotenzialen in Prozessen. Bestehende Ansätze benötigen Experten, welche Potenziale über ihre Erfahrung oder zeitaufwendig mithilfe von Musterkatalogen identifizieren. In diesem Artikel werden verschiedene Digitalisierungspotenziale klassifiziert und Muster für ein zukünftiges musterbasiertes Analyseverfahren zur automatisierten Identifikation von Digitalisierungspotenzialen in BPMN-Diagrammen beschrieben. Im Vergleich zu bestehenden Ansätzen erlaubt es Experten die Identifizierung von Digitalisierungspotenzialen effizienter und effektiver durchzuführen.

Many websites offer links to social media sites for convenient content sharing. Unfortunately, those sharing capabilities are quite restricted and it is seldom possible to share content with other services, like those provided by a user's favorite applications or smart devices. In this paper, we present Semantic Data Mediator (SDM) --- a flexible middleware linking a vast number of services to millions of websites. Based on reusable repositories of service descriptions defined by the crowd, users can easily fill a personal registry with their favorite services, which can then be linked to websites by SDM. For this, SDM leverages semantic data, which is already available on millions of websites due to search engine optimization. Further support for our approach from website or service developers is not required. To enable the use of a broad range of services, data conversion services are automatically composed by SDM to transform data according to the needs of the different services. In addition to linking web services, various service adapters allow services of applications and smart devices to be linked as well. We have fully implemented our approach and present a real-world case study demonstrating its feasibility and usefulness.

Software companies nowadays create ecosystems of users and third-party providers around their platforms. They often provide online stores so that the third-party developments can be exposed to users directly. The resulting ecosystems differ significantly from each other in their architectural designs because their providers differ in terms of business goals and contexts. Until now, this architectural diversity and rationale behind it are not well-understood. Therefore, it is not clear which software features contribute to ecosystem’s success with respect to certain business goals and context. This hinders systematic creation of ecosystems in the future. Thus, decision-making becomes too risky; for future ecosystem providers, which may lead to creation of inefficient ecosystems that lack critical features, and for third-party providers to rely on ad-hoc choices while deciding on suitability of an ecosystem for their future career. In this paper, we introduce three design patterns for store- oriented software ecosystems by classifying the design decisions, business goals, and context of 111 store-oriented software ecosystems. Each design pattern provides an architectural solution to achieve a different business goal while supporting a different context. We discuss how the design patterns are applied together in order to achieve more business goals. Our work supports ecosystem and third-party providers by sharing practice-proven architectural solutions, helping them to take informed architectural decisions and reduce technical risks.

Pioneers of today’s software industry like Salesforce and Apple have established successful ecosystems around their software platforms. Architectural knowledge of the existing ecosystems is implicit and fragmented among online documentation. In protection of intellectual property, existing documentation hardly reveals influential business strategies that affect the ecosystem structure. Thus, other platform providers can hardly learn from the existing ecosystems in order to systematically make reasonable design decisions with respect to their business strategies to create their own ecosystems. In this paper, we identify a variability model for architectural design decisions of a store-oriented software ecosystem product line from an enterprise perspective, comprising business, application, and infrastructure views. We derive the variability model from fragmentary material of existing ecosystems and a rigorous literature review using a research method based on the design science paradigm. To show its validity, we describe real-world ecosystems from diverse domains using the variability model. This knowledge helps platform providers to develop customized ecosystems or to recreate existing designs in a systematic way. This, in turn, contributes to an increase in designer and developer productivity.

The Internet of Things (IoT) connects the things of our everyday life and supports usin our common activities.Several markets for IoT services have been created. These markets enable IoT users to search and compose services in order to support an IoT activity.However, existing IoT markets like IFTTT (If This Then That) are not convenient for users with respect to service discovery and composition.The objective of On-the-fly (OTF) computing is to configure and provide software markets that fulfill individual users' wishes by the automatic on-the-fly composition of single services.The architecture framework of On-the-fly computing markets helps architects to systematically develop these systems in different domains.In this paper, we use our OTF architectural framework to examine the requirements of a reference architecture for IoT markets.Furthermore, we perform a comparison between the architecture of IFTTT as an existing IoT market with this reference architecture. The results show how existing IoT markets can be improved. In return, the practical knowledge of IFTTT is taken to the reference architecture.This knowledge helps to overcome the limitations of today's IoT markets or creating new markets in the future.

Bridging the gap between informal, imprecise, and vague user requirements descriptions and precise formalized specifications is the main task of requirements engineering. Techniques such as interviews or story telling are used when requirements engineers try to identify a user's needs. The requirements specification process is typically done in a dialogue between users, domain experts, and requirements engineers. In our research, we aim at automating the specification of requirements. The idea is to distinguish between untrained users and trained users, and to exploit domain knowledge learned from previous runs of our system. We let untrained users provide unstructured natural language descriptions, while we allow trained users to provide examples of behavioral descriptions. In both cases, our goal is to synthesize formal requirements models similar to statecharts. From requirements specification processes with trained users, behavioral ontologies are learned which are later used to support the requirements specification process for untrained users. Our research method is original in combining natural language processing and search-based techniques for the synthesis of requirements specifications. Our work is embedded in a larger project that aims at automating the whole software development and deployment process in envisioned future software service markets.

Within software engineering, requirements engineering starts from imprecise and vague user requirements descriptions and infers precise, formalized specifications. Techniques, such as interviewing by requirements engineers, are typically applied to identify the user’s needs. We want to partially automate even this first step of requirements elicitation by methods of evolutionary computation. The idea is to enable users to specify their desired software by listing examples of behavioral descriptions. Users initially specify two lists of operation sequences, one with desired behaviors and one with forbidden behaviors. Then, we search for the appropriate formal software specification in the form of a deterministic finite automaton. We solve this problem known as grammatical inference with an active coevolutionary approach following Bongard and Lipson [2]. The coevolutionary process alternates between two phases: (A) additional training data is actively proposed by an evolutionary process and the user is interactively asked to label it; (B) appropriate automata are then evolved to solve this extended grammatical inference problem. Our approach leverages multi-objective evolution in both phases and outperforms the state-of-the-art technique [2] for input alphabet sizes of three and more, which are relevant to our problem domain of requirements specification.

A lot of people are managing multiple computing devices suited for different purposes, like private and work devices. Integrating applications running on different devices is often a problem, because the services provided by those applications are not meant to be integrated. In this demonstration, we present our XDAI-A framework which enables cross-device integration of services provided by Android apps. The framework uses adapters to convert Android-internal service interfaces of existing apps into external services with a platform-independent interface that can be accessed from applications on other devices and even other platforms. Our ready-to-use framework does not require to alter existing Android apps, since the adapters are installed separately. For the convenient specification of adapters, our framework comes with a domain-specific language (DSL). Additionally, we provide an infrastructure to find and integrate devices and their applications' services.