Cyber-Physical Systems change at runtime, so errors are very difficult to trace. The Information Flow Monitor is a tool that captures semantic dependencies between exchanged information. To do so, we use spy nodes as observing instances distributed throughout the network. The positioning of the spies is thus important to cover as many information paths as possible. In this paper, we examine guidelines to achieve high path coverage with as less as possible spies. Using an evolutionary algorithm, a machine learning technique, we develop a metaheuristic that enables us to quickly select such spy sets.

The Information Flow Monitor (IFM) is a protocol and tool that can record dependencies between exchanged information in Cyber-Physical Systems (CPS). This makes it possible to determine the original source of faulty information in case of an error and to correct errors at their origin. However, the IFM protocol also requires additional resources in terms of processing time and network traffic. In this paper, we measure the additional resources required using an example network and discuss how these resources can affect the operation of a CPS.

Observing semantic dependencies in large and heterogeneous networks is a critical task, since it is quite difficult to find the actual source of a malfunction in the case of an error. Dependencies might exist between many network nodes and among multiple hops in paths. If those dependency structures are unknown, debugging errors gets quite difficult. Since CPS and other large networks change at runtime and consists of custom software and hardware, as well as components off-the-shelf, it is necessary to be able to not only include own components in approaches to detect dependencies between nodes. In this paper we present an extension to the Information Flow Monitor approach. Our goal is that this approach should be able to handle unalterable blackbox nodes. This is quite challenging, since the IFM originally requires each network node to be compliant with the IFM protocol.

Cyber-Physical Systems (CPS) are interconnected systems, that adapt to their environment. They are quite challenging to engineer and to test, because the its interconnected and networked structures changes at runtime. Dependencies and influencing between nodes in the network might be difficult to test, because there are many hidden impacts on not directly connected nodes. In this paper, we present our experimentation environment which we use to simulate different CPSs. The focus of the development of this environment is to be able to rapidly generate, deploy and change software and network connections within the CPS and to observe resulting impacts on the network.

Developing CPS means uniting multiple engineering disciplines: mechanical, electrical and software engineering. Each of them provides a set of models, guidelines and processes crucial for a projects success. Consequently, CPS development is heavily influenced by processes known in either engineering discipline. There is currently no coherent software engineering approach to develop CPS, making it very hard to align the aforementioned domain experts in a controlled and repeatable process. This paper takes a step towards filling this gap by providing the insights during the development of a CPS in the form of an experience report. Based on our findings we validated the use of the Double TwinPeaks model and contribute to the field of software engineering for CPS the Matrix of Granularity to assess the components of a CPS in terms of customizability, risk and constraints.

Cyber-Physical Systems are distributed, heterogeneous, decentralized and loosely coupled networks in which individual systems measure physical processes, exchange information, and influence processes. Sensors measure these physical processes, while aggregators process them and actuators perform resulting actions. Decisions are often based on sensor data collected by other systems. Furthermore, the aggregators also interchange information and use them to derive own decisions. Decisions must be comprehensible. However, this is only the case if all data dependencies are known. Due to the size of these networks, their loose coupling and their dynamic behavior, decisions made by a system are not always easy to understand. If an error occurs in the system, the error source must be identified. It must be known on which data a decision was based. However, since the decision can be based on information from other nodes, the search for the error source is not a trivial task. Keep in mind, that dependent nodes can have dependencies themselves as well. We present the Information Flow Monitor (IFM) that collects information about semantic data dependencies in dynamic networks. The collected dependency information is provided at a central network location. Subsequently, semantic dependencies between information can be visualized.

Cyber-physical systems are context-aware networked systems containing sensors, aggregators and actuators. Raw sensor data and aggregated information are spread among the network and processed in multiple nodes to result in an action, which is possibly executed at a different physical location in the network. Due to the flexible topology and the emergent features of CPS, decisions within the network that trigger actions are not always trivial to understand. These decisions are based on raw sensor data which are not clearly visible at the location of their execution. This can be problematic if decisions have to be justifiable. It becomes even more difficult if decisions has been flawed, and it is not ascertainable why they were made. In order to determine the reasons for incorrect decisions in the network, the dependencies and input values of a decision must be known. Without this information, debugging is quite difficult. In this paper, we present the Information Flow Monitor, which can capture and visualize dependencies between nodes and information in CPS.

Connecting digital information systems with real world objects and processes is the core of the digital transformation. New business models and opportunities thrive on the various options that the resulting information offers. Cyber-Physical Systems (CPS) are the most prominent incarnation and – in contrast to various other aspects of the digital transformation – really new: sensors and actors allow information systems to monitor the real world and will profoundly change most markets and business domains. However, developing CPS involves different specialists and various technical challenges. Software and hardware engineers, network specialists, and data scientists have to work hand in hand and combine their specialties to incorporate the different perspectives into one team. We present EngCPS, an engineering approach to develop CPS that enhances classic software engineering methods with CPS-specific extensions.

Touch- and gesture-based interfaces are common in applications for mobile devices. By evolving into mass market products, smartphones and tablets created an increased need for specialized software engineering methods. To ensure high quality applications, constant and efficient testing is crucial in software development. However, testing mobile applications is still cumbersome, time-consuming and error-prone. One reason is the devices' focus on touch-based interaction - gestures cannot be easily incorporated into automated application tests. We present an extension to the popular Calabash testing framework solving this problem by allowing to describe gestures with a formal language in tests scripts.