While formal verification techniques are inevitable to ensure safety of critical cyber-phyical systems (CPS), engineering techniques to support the design and analysis of such CPS are still in their infancy. Therefore, we take a first step towards the provision of appropriate engineering techniques for CPS verification, by providing an extensive evaluation of the current state of the art, identifying challenges not yet tackled by existing approaches and by proposing a research roadmap intended to pave the way towards a fully supported engineering process for CPS verification models.

Abstract Executable domain-specific languages (DSLs) enable the execution of behavioral models. While an execution is mostly driven by the model content (e.g., control structures), many use cases require interacting with the running model, such as simulating scenarios in an automated or interactive way, or coupling the model with other models of the system or environment. The management of these interactions is usually hardcoded into the semantics of the DSL, which prevents its reuse for other DSLs and the provision of generic interaction-centric tools (e.g., event injector). In this paper, we propose a metalanguage for complementing the definition of executable DSLs with explicit behavioral interfaces to enable external tools to interact with executed models in a unified way. We implemented the proposed metalanguage in the GEMOC Studio and show how behavioral interfaces enable the realization of tools that are generic and thus usable for different executable DSLs.

Efficient and safe operation of Intelligent Transportation Systems (ITS) has significant impact on the life of citizens and economies. A wide range of technologies, aka. Operational Technologies (OT) (e.g., sensors, actuators or network routers), are employed for monitoring and controlling the operation of ITS, being, due to the convergence with Information Technology (IT), more and more based on the Internet-of-Things (IoT) paradigm. Monitoring the service quality of OT, i.e., Operational Technology Monitoring (OTM), is a crucial prerequisite for ensuring efficient and safe operation. OTM from an industry perspective, however, faces some challenges, which are, despite their importance for ITS operators, not sufficiently addressed by now.

Since the emergence of Bitcoin, blockchains (BCs) have been applied not only in the finance sector, but also in various other domains like health care, education or Industry 4.0 resulting in numerous different BC platforms and substantial research work. This plenty of efforts yielded to several valuable scientific surveys classifying and evaluating existing BC platforms. Although each of them puts forward a somewhat consolidated view on the field, it is still challenging to get rid of the "blockchain muddle" preventing even a common perception of the core functionality of BCs. Instead of providing yet another BC overview we conduct a meta survey of existing BC studies and report on lessons learned in this paper, being the basis for our vision towards a UML-based reference model considering both, structural and behavioral aspects of BCs and thereby identifying the nucleus of BCs.

Social Media services gain increasing importance as a new data source for achieving Situation Awareness in disaster management. One crucial prerequisite is to automatically filter social media messages towards informativeness commonly realized by supervised machine learning. Since disaster situations are different, most classification approaches focus on informativeness classification of similar disasters. Thus their use is limited to particular disaster types, for instance earthquakes or floods, lacking general applicability. At the same time, how to get accurate informativeness classification for new disaster events is not yet totally understood due to variations in training data, features, classification algorithms and their settings. To address these issues, our contribution is threefold: First, a systematic and in-depth analysis of an existing twitter crisis data set is provided along four different dimensions in order to gain a comprehensive understanding of those characteristics indicating informative Tweets in disaster situations. On basis of these insights, a cross domain classifier is engineered, which is applicable not only across different disaster events but also across disaster events of different types. Finally, systematic classification experiments are conducted, demonstrating that our classification approach is more accurate than other disaster type specific ones.

We present an approach for hybrid systems that combines the advantages of component-based modeling (e.g., reduced model complexity) with the advantages of formal verification (e.g., guaranteed contract compliance). Component-based modeling can be used to split large models into multiple component models with local responsibilities to reduce modeling complexity. Yet, this only helps the analysis if verification proceeds one component at a time. In order to benefit from the decomposition of a system into components for both modeling and verification purposes, we prove that the safety of compatible components implies safety of the composed system. We implement our composition theorem as a tactic in the KeYmaera X theorem prover, allowing automatic generation of a KeYmaera X proof for the composite system from proofs for the components without soundness-critical changes to KeYmaera X. Our approach supports component contracts (i.e., input assumptions and output guarantees for each component) that characterize the magnitude and rate of change of values exchanged between components. These contracts can take into account what has changed between two components in a given amount of time since the last exchange of information.

Volunteering is an indispensable cornerstone of our society, covering nearly every part of our life, from social care to emergency management and education. This omnipresence of volunteering led to a plethora of volunteer management systems (VMS), mainly supporting NPOs in scheduling and allocating tasks to volunteers. In contrast to this NPO-centric approach of existing VMS, we focus on volunteers by investigating the following core question: "How can the engagement of volunteers be digitized and exploited in a life-long way". To provide a first step towards answering this question, the contribution of this paper is threefold. First, an in-depth study of related approaches is provided identifying shortcomings but also promising concepts. Second, challenges which have to be tackled to deal with the broad spectrum and peculiarities of volunteering are identified and a vision for a next-generation VMS called iVolunteer is pointed out. Third, promising technologies are identified and discussed in detail to lay the basis for the technical architecture of our envisioned iVolunteer-prototype.

Evolving metamodels are in the center of Model-Driven Engineering, necessitating the co-evolution of dependent artifacts like models and transformations. While model co-evolution has been extensively studied, transformation co-evolution has received less attention up to now. Current approaches for transformation co-evolution provide a fixed, restricted set of metamodel (MM) changes, only. Furthermore, composite changes are treated as monolithic units, which may lead to inconsistent co-evolution for overlapping atomic changes and prohibits extensibility. Finally, transformation co-evolution is considered in isolation, possibly inducing inconsistencies between model and transformation co-evolution. To overcome these limitations, we propose a complete set of atomic MM changes being able to describe arbitrary MM evolutions. Reusability and extensibility are supported by means of change composition, ensuring an intra-artifact consistent co-evolution. Furthermore, each change provides resolution actions for both, models and transformations, ensuring an inter-artifact consistent co-evolution. Based on our conceptual approach, a prototypical implementation is presented.

Model transformations play a vital role in Model-Driven Engineering. Due to their increasing complexity, proper means for ensuring their quality are needed. Although numerous approaches for testing of model transformations have been proposed, their focus is rather on formal verification than on execution-based testing. Additionally, existing approaches do not consider the actual transformation definition, rarely provide hints for debugging in case of an error and are specific to a certain transformation language. Therefore we propose TETRA Box as a generic framework for execution-based white-box testing of transformation languages. For this, we base on symbolic execution of model transformations. Additionally, by employing our Pattern-based Modeling Language for Model Transformations (PaMoMo), we are able to provide dedicated failure traces that are aligned to the actual transformation definition as a hint for debugging.

Model-Driven Engineering places models as first-class artifacts throughout the software lifecycle requiring the availability of proper model transformation languages. Although numerous languages are available, they lack convenient facilities for debugging and supporting understanding of the transformation logic. This is not least because the underlying transformation engines operate on a low level of abstraction, hiding the operational semantics of a high-level language. Consequently, low-level debugging information is available only, e.g., variable values. To tackle these limitations, we propose a DSL on top of Colored Petri Nets (CPNs) - called Transformation Nets (TNs) - for the execution and debugging of model transformations. By integrating all artifacts of a transformation, i.e., metamodel elements, model elements, and transformation logic, a runtime model for model transformations is provided, making the afore hidden operational semantics explicit. Based on this runtime model we present various means for debugging by means of an example showing how a QVT-Relations (QVT-R) specification may be debugged using TNs.

Workflows play a major role in nowadays business and therefore its requirement elicitation must be accurate and clear for achieving the closest solution to business's needs. Due to Web applications popularity, the Web is becoming the standard platform for implementing business workflows. In this context, Web applications and their workflows must be adapted to market demands in such a way to minimize development effort. In this work we present a model-driven approach for specifying Web workflows adaptations using a Domain Specific Language for Web application requirement called WebSpec. We present an extension to WebSpec based on Pattern Specifications for dealing with crosscutting workflow requirements by identifying tangled and scattered behaviour and reducing inconsistencies early in the requirement gathering phase. Using simple but illustrative examples we show the expressive power of the approach.

Business processes face constantly changing context factors like varying customer behavior or market conditions that force to adapt the underlying workflows to these evolving situations. Information overload induced by the diversity of context factors, however, leads to the inability to provide coherently modeled, comprehensible, and re-usable workflows and the failure to recognize relevant situations in time. The main goal of our research project ProFlow is to leverage situation awareness in all phases of workflow management especially focusing on dynamic spatial systems as encountered, e.g., in the domain of road traffic management. ProFlow thereby bases on a generic ontology-driven framework for situation perception and comprehension. This paper details on the corresponding ontological representations especially addressing extension points that allow developers to extend and configure our framework for their own application domains. This forms the basis for the overall system architecture, which is laid out along its prototypical implementation.

Model transformations play a key role in the vision of Model-Driven Engineering. Thereby, the resolution of structural heterogeneities between metamodels (MMs) represents the key challenge. For this task, current approaches require the definition of partly tricky, low-level recurring transformation logic but neglect to offer reusable components. Moreover, little attention has been paid to heterogeneities caused by the concept of inheritance, although extensively used in MMs. Therefore, we propose to specify model transformations in a plug & and play manner by a set of pre-defined mapping operators (MOps) representing a DSL to resolve structural heterogeneities. For coping with inheritance in MMs, we introduce an inheritance mechanism between MOps allowing to reuse parts of the mapping definitions. Moreover, dedicated MOps for resolving heterogeneities when one MM comprises inheritance hierarchies whereas the other one does not are presented, which are well-known problems in object-relational transformations and object-oriented refactorings.

OMG's model-driven architecture (MDA) has emerged as a new approach for the development of software. For this, the query/view/transformation (QVT) standard plays a central role, since it allows for the specification of model transformations. Nevertheless, until now, QVT-tool support in general and debugging support in particular in the context of MDA are rather limited, supposable being a reason, that the adoption of QVT in practice has not yet been achieved. We therefore propose graphical debugging for the QVT Relations language based on TROPIC - a model transformation approach on the basis of coloured Petri nets. By enabling debugging on the TROPIC level, one gains several advantages when developing transformations. Firstly, debugging can take place at a high level of abstraction. Secondly, it serves for explicating the operational semantics of a transformation. Thirdly, it provides a homogenous representation of all transformation artifacts. As a first step towards QVT debugging, this paper aims at a deeper understanding of the operational semantics of QVT, classifying common pitfalls by using QVT and discussing how they may be identified at the TROPIC level.

In the Model-Driven Architecture (MDA) paradigm the Query/View/Transformation (QVT) standard plays a vital role for model transformations. Especially the high-level declarative QVT Relations language, however, has not yet gained widespread use in practice. This is not least due to missing tool support in general and inadequate debugging support in particular. Transformation engines interpreting QVT Relations operate on a low level of abstraction, hide the operational semantics of a transformation and scatter metamodels, models, QVT code, and trace information across different artifacts. We therefore propose a model-based debugger representing QVT Relations on bases of TROPIC, a model transformation language utilizing a variant of Colored Petri Nets (CPNs). As a prerequisite for convenient debugging, TROPIC provides a homogeneous view on all artifacts of a transformation on basis of a single formalism. Besides that, this formalism also provides a runtime model, thus making the afore hidden operational semantics of the transformation explicit. Using an explicit runtime model allows to employ model-based techniques for debugging, e.g., using the Object Constraint Language (OCL) for simply defining breakpoints and querying the execution state of a transformation.