https://www.mdu.se/

Defects with low manifestation probability, such as concurrency defects, are difficult to find during testing. When such a defect manifests into an error, the low likelihood can make it time-consuming to reproduce the error and find the root cause. Static Code Analysis (SCA) tools have been used in the industry for decades, mostly for compliance checking towards guidelines such as MISRA. Today, these tools are capable of sophisticated data and execution flow analysis. Our work, presented in this paper, evaluates the feasibility of using SCA tools for concurrency defect detection and localization. Earlier research has categorized concurrency defects. We use this categorization and develop an object-oriented C++ based test suite containing defects from each category. Secondly, we use known and real defects in existing products' source code. With these two approaches, we perform the evaluation, using tools from some of the largest commercial actors in the field. Based on our results, we provide a discussion about how to use static code analysis tools for concurrency defect detection in complex embedded real-Time systems.

Distributed control systems transform with the Industry 4.0 paradigm shift. A mesh-like, network-centric topologyreplaces the traditional controller-centered architecture, enforcing the interest of cloud-, fog-, and edge-computing, where lightweight container-based virtualization is a cornerstone. Kubernetes is a well-known container management system for container orchestration in cloud computing. It is gaining traction inedge- and fog-computing due to its elasticity and failure recovery properties. Orchestrator failure recovery can complement the manual replacement of a failed controller and, combined with controller redundancy, provide a pseudo-one-out-of-many redundancy. This paper investigates the failure recovery performance obtained from an out-of-the-box Kubernetes installation in a distributed control system scenario. We describe a Kubernetes based virtualized controller architecture and the software needed to setup a bare-metal cluster for control systems. Further, we deploy single and redundant configured containerized controllers based on an OPC UA compatible industry middleware software on the bare-metal cluster. The controllers expose variables with OPCUA PubSub. A script-based daemon introduces node failures, and a verification controller measures the downtime when using Kubernetes with an industry redundancy solution