In software engineering, behavioral state machine models play a crucial role in validating system behavior and maintaining correctness. This paper proposes an extension of an existing architecture for automatically learning state machine models of client-server systems that automates processes such as regression detection and test case generation, and guides the development of new features. The learned models help identify potential implementation issues of clients, servers, their interactions, as well as the protocols themselves. The architecture also enhances the debugging process and ensures comprehensive system coverage. By employing the LTSDiff algorithm, the method efficiently detects behavioral changes due to software updates, preventing unintended consequences on system performance. Consequently, the automatically generated state machine models can be used as evidence in security, safety, and reliability assurance, providing a valuable tool for development, testing, and maintenance of complex software systems. The learned state machines and detected changes correctly model the behavior of a client-server system to a specified depth at the level of an active outside adversary with the capability to read, replay, replace, or block any message.

The challenges in implementing SAE Level 4/5 automated vehicles are manifold, with intersection navigation being a pervasive one. We analyze a novel road topology invented by a co-author of this paper, Xiayong Hu. The topology eliminates the need for traditional traffic control and cross-traffic at intersections, potentially improving the safety of autonomous driving systems. The topology, herein called the Zonal Road Topology, consists of unidirectional loops of road with traffic flowing either clockwise or counter-clockwise. Adjacent loops are directionally aligned with one another, allowing vehicles to transfer from one loop to another through a simple lane change. To evaluate the Zonal Road Topology, a one km2pilot-track near Changshu, China is currently being set aside for testing. In parallel, traffic simulations are being performed. To this end, we conduct a simulation-based comparison between the Zonal Road Topology and a traditional road topology for a generic Electric Vehicle (EV) using the Simulation for Urban MObility (SUMO) platform and MATLAB/Simulink. We analyze the topologies in terms of their travel efficiency, safety, energy usage, and capacity. Drive time, number of halts, progress rate, and other metrics are analyzed across varied traffic levels to investigate the advantages and disadvantages of the Zonal Road Topology. Our results indicate that vehicles on the Zonal Road Topology have a lower, more consistent drive time, make more progress, and halt less frequently, while using less energy on average. The Zonal Road Topology also has the capacity to support a greater amount of vehicles. These results become more prominent at higher traffic densities.

Awareness of the importance of systems and software traceability, as well as tool support for traceability, have improved over the years. But an effective solution for traceability must align and integrate with an organization’s engineering processes. Specifically, the phases of the traceability process model (traceability strategy, creation, use and maintenance of traceability) must be aligned with the organization’s engineering processes. Previous research has discussed the benefits of integrating traceability into the configuration management process. In this paper, we propose Change Request management using traceability data. In our approach, new Change Requests (CRs) are created from the traceability model of the corresponding project. The created CRs contain a portion of the project’s overall traceability model that is relevant to that change. A proof-of-concept issue tracking system is proposed that uses a traceability model at its core.

In this paper we draw on our experience in the automotive industry to portray the clear need for proper documentation of Simulink models when they describe the implementations of embedded systems. We effectively discredit the “model is documentation” motto that has been hounding the model-based paradigm of software development. The state of the art of documentation of Simulink designs of embedded systems, both in academia and industrial practice, is reviewed. We posit that lack of proper documentation is costing industry dearly, and propose that a significant change in development culture is needed to properly position documentation within the software development process. Further, we discuss what is required to foster such a culture.