Symbolic execution is an SMT-based software verification and testing technique. Symbolic execution requires tracking performed computations during software simulation to reason about branches in the software under test. The prevailing approach on symbolic execution of binary code tracks computations by transforming the code to be tested to an architecture-independent intermediate representation (IR) and then symbolically executes this IR. However, the resulting IR must be semantically equivalent to the binary code, making this process complex and error-prone. The semantics of the binary code are specified by the targeted instruction set architecture (ISA), commonly given in natural language and requiring a manual implementation of the transformation to an IR. In recent years, the use of formal languages to describe ISA semantics in a machine-readable way has gained increased popularity. We investigate the utilization of such formal semantics for symbolic execution of binary code, achieving an accurate representation of instruction semantics. We present a prototype for the RISC-V ISA and conduct a case study to demonstrate that it can be easily extended to additional instructions. Furthermore, we perform an experimental comparison with prior work which resulted in the discovery of five previously unknown bugs in the ISA implementation of the popular IR-based symbolic executor angr.

The development process for new embedded systems relies increasingly on simulation, e.g. to develop hardware and software components in parallel using virtual prototyping. The central component of a virtual prototype is the instruction set simulator (ISS) which implements instruction execution for a specific instruction set architecture (ISA). To avoid erroneous behavior during software simulation, it is paramount to ensure that the provided ISS implements the ISA exactly as specified, i.e. that there are no discrepancies between the hardware and the VP. In order to increase confidence in the correctness of the VP's ISS, it is advantageous to generate it automatically from a formal model of the ISA instead of implementing it manually. While a variety of formal ISA models have been proposed in prior work, they are presently not widely used in the VP domain. We attempt to ease employment of formal models for ISS generation in this domain. To this end, we reduce the integration effort through a simulator-agnostic ISS generation approach that integrates well with existing simulators and existing vendor-supplied VP components. Our approach leverages a formal RISC-V ISA model which exclusively describes instruction semantics and abstracts interactions with hardware components through an interface model, thus encapsulating interactions with simulator-specific code. As part of our experiments, we were able to generate an ISS for the popular RISC-V implementations Spike and RISC-V VP, thereby replacing their manually written implementations. Performed benchmarks indicate that the generated ISS offers the same simulation performance as a manually written one, while still passing the official RISC-V tests.

Urban mobility is currently undergoing three fundamental transformations with the sharing economy, electrification, and autonomous vehicles changing how people and goods move across cities. In this paper, we demonstrate the valuable contribution of decision support systems that combine data-driven analytics and simulation techniques in understanding complex systems such as urban transportation. Using the city of Berlin as a case study, we show that shared, autonomous electric vehicles can substantially reduce resource investments while keeping service levels stable. Our findings inform stakeholders on the trade-off between economic and sustainability-related considerations when fostering the transition to sustainable urban mobility.

In this paper, we analyze possible interferences that occur when developing information systems for cyberphysical systems – hybrids containing legacy technical components and added IT modules. In particular, the enhancement of electricity systems through IT components, resulting in smart grid and smart home solutions, is experiencing increasing adoption rates in advanced and developing countries alike. Due to the substantial interdependence with and reliance on often decades-old legacy infrastructure, these cyberphysical systems present particular opportunities and challenges for the associated information systems. We propose that one channel through which information systems improve cyberphysical systems is by enhancing the components of the legacy system with new features. Introducing the Design–Interference Model, we conceptualize IS design for cyberphysical systems and outline possible obstacles and interferences associated with it. Using a simulation experiment for a smart grid application as a showcase, we derive insights into how to address these challenges. We condense these results into actionable advice that, first, seeks to validate existing guidelines for design-oriented research, second, extends and focuses them on cyberphysical system applications and, third, provides guidance to researchers and practitioners in this increasingly relevant area of information system development.

Machine Condition Monitoring (MCM) is an important topic for the reliability of industrial machines in increasingly interconnected production facilities. The analysis of a huge amount of data to get information about the machine's condition is a difficult challenge. Current solutions for these analyses are often very specific, need a lot of manual configuration or are difficult to apply. In this paper, we present a system that uses anomaly detection in data streams to find hints for faulty machines in the data. The basis of this system is a Data stream management system (DSMS), which can handle huge amounts of streaming data and simplifies the definition of analyses. Due to the anomaly detection algorithms, the approach can be applied to a variety of data and scenarios. The outcome is a system that allows live analysis of machine data for MCM.