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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.

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.

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.

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.

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.

Websites increasingly embed semantic data for search engine optimization. The most common ontology for semantic data, schema.org, is supported by all major search engines and describes over 500 data types, including calendar events, recipes, products, and TV shows. As of today, users wishing to pass this data to their favorite applications, e.g., their calendars, cookbooks, price comparison applications or even smart devices such as TV receivers, rely on cumbersome and error-prone workarounds such as reentering the data or a series of copy and paste operations. In this paper, we present Semantic Data Mediator (SDM), an approach that allows the easy transfer of semantic data to a multitude of services, ranging from web services to applications installed on different devices. SDM extracts semantic data from the currently displayed web page on the client-side, offers suitable services to the user, and by the press of a button, forwards this data to the desired service while doing all the necessary data conversion and service interface adaptation in between. To realize this, we built a reusable repository of service descriptions, data converters, and service adapters, which can be extended by the crowd. Our approach for linking services to websites relies solely on semantic data and does not require any additional support by either website or service developers. We have fully implemented our approach and present a real-world case study demonstrating its feasibility and usefulness.

Existing software markets like Google Play allow users to search among available Apps and select one based on the description provided for the App or based on its rating. Future software markets facilitate on-the-fly composition of such Apps based on users’ individual wishes. Realizing such On-The-Fly Computing (OTF) markets requires support of sophisticated software features. In addition, suitable orchestration among such features needs to ensure well-alignment of business and IT aspects in case of run-time changes like market dynamics. However, all these introduce new architectural and management complexities, which are specific to such markets. An architecture framework for OTF markets will include design solutions to overcome these complexities. In my PhD, I aim at identifying an architecture framework for OTF markets including main architectural building blocks and a systematic development process. Such an architecture framework enables the development of OTF markets in the future. Furthermore, this knowledge can be used as a basis to improve existing software markets by integrating missing functionalities.

The provision of IT solutions over electronic marketplaces became prominent in recent years. We call such marketplaces IT service markets. IT service markets have some core architectural building blocks that impact the quality attributes of these markets. However, these building blocks and their impacts are not well-known. Thus, design choices for IT service markets have been made ad-hoc until now. Furthermore, only single aspects of such markets have been investigated until now, but a comprehensive view is missing.In this paper, we identify common features and their interrelations on the basis of a systematic literature review of 60 publications using grounded theory.This knowledge provides an empirical evidence on the interdisciplinary design choices of IT service markets and it serves as a basis to support market providers and developers to integrate market features. Thereby, we make a first step towards the creation of a reference model for IT service markets that provides a holistic integrated view that can be used to create and maintain successful markets in the future.

Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation. Its knowledge is increasingly used within Wikipedia itself and various other kinds of information systems, imposing high demands on its integrity.Wikidata can be edited by anyone and, unfortunately, it frequently gets vandalized, exposing all information systems using it to the risk of spreading vandalized and falsified information. In this paper, we present a new machine learning-based approach to detect vandalism in Wikidata.We propose a set of 47 features that exploit both content and context information, and we report on 4 classifiers of increasing effectiveness tailored to this learning task. Our approach is evaluated on the recently published Wikidata Vandalism Corpus WDVC-2015 and it achieves an area under curve value of the receiver operating characteristic, ROC-AUC, of 0.991. It significantly outperforms the state of the art represented by the rule-based Wikidata Abuse Filter (0.865 ROC-AUC) and a prototypical vandalism detector recently introduced by Wikimedia within the Objective Revision Evaluation Service (0.859 ROC-AUC).

We apply methods of genetic programming to a general problem from software engineering, namely example-based generation of specifications. In particular, we focus on model transformation by example. The definition and implementation of model transformations is a task frequently carried out by domain experts, hence, a (semi-)automatic approach is desirable. This application is challenging because the underlying search space has rich semantics, is high-dimensional, and unstructured. Hence, a computationally brute-force approach would be unscalable and potentially infeasible. To address that problem, we develop a sophisticated approach of designing complex mutation operators. We define ‘patterns’ for constructing mutation operators and report a successful case study. Furthermore, the code of the evolved model transformation is required to have high maintainability and extensibility, that is, the code should be easily readable by domain experts. We report an evaluation of this approach in a software engineering case study.

Integrating apps on mobile devices into applications running on other devices is usually difficult. For instance, using a messenger on a smartphone to share a text written on a desktop computer often ends up in a cumbersome solution to transfer the text, because many applications are not designed for such scenarios. In this paper, we present an approach enabling the integration of apps running on Android devices into applications running on other devices and even other platforms. This is achieved by specifying adapters for Android apps, which map their services to a platform-independent service interface. For this purpose, we have developed a domain-specific language to ease the specification of such mappings. Our approach is applicable without the need to modify the existing Android apps providing the service. We analyzed its feasibility by implementing our approach and by specifying mappings for several popular Android apps, e.g., phone book, camera, and file explorer.

A task at the beginning of the software development process is the creation of a requirements specification. The requirements specification is usually created by a software engineering expert. We try to substitute this expert by a domain expert (the user) and formulate the problem of creating requirements specifications as a search-based software engineering problem. The domain expert provides only examples of event sequences that describe the behavior of the required software program. These examples are represented by simple sequence diagrams and are divided into two subsets: positive examples of required program behavior and negative examples of prohibited program behavior. The task is then to synthesize a generalized requirements specification that usefully describes the required software. We approach this problem by applying a genetic algorithm and evolve deterministic finite automata (DFAs). These DFAs take the sequence diagrams as input that should be either accepted (positive example) or rejected (negative example). The problem is neither to find the minimal nor the most general automaton. Instead, the user should be provided with several appropriate automata from which the user can select, or which help the user to refine the examples given initially. We present the context of our research ("On-The-Fly Computing"), present our approach, report results indicating its feasibility, and conclude with a discussion.

Today, cloud vendors host third party black-box services, whose developers usually provide only textual descriptions or purely syntactical interface specifications. Cloud vendors that give substantial support to other third party developers to integrate hosted services into new software solutions would have a unique selling feature over their competitors. However, to reliably determine if a service is reusable, comprehensive service specifications are needed. Characteristic for comprehensive in contrast to syntactical specifications are the formalization of ontological and behavioral semantics, homogeneity according to a global ontology, and a service grounding that links the abstract service description and its technical realization. Homogeneous, semantical specifications enable to reliably identify reusable services, whereas the service grounding is needed for the technical service integration. In general, comprehensive specifications are not availableand have to be derived. Existing automatized approaches are restricted to certain characteristics of comprehensiveness. In my PhD, I consider an automatized approach to derive fully-fledged comprehensive specifications for black-box services. Ontological semantics are derived from syntactical interface specifications. Behavioral semantics are mined from call logs that cloud vendors create to monitor the hosted services. The specifications are harmonized over a global ontology. The service grounding is established using traceability information. The approach enables third party developers to compose services into complex systems and creates new sales channels for cloud and service providers.