Formal methods encompass a wide choice of techniques and tools for the specification, development, analysis, and verification of software and hardware systems. Formal methods are widely applied in industry, in activities ranging from the elicitation of requirements and the early design phases all the way to the deployment, configuration, and runtime monitoring of actual systems. Formal methods allow one to precisely specify the environment in which a system operates, the requirements and properties that the system should satisfy, the models of the system used during the various design steps, and the code embedded in the final implementation, as well as to express conformance relations between these specifications. We present a broad scope of successful applications of formal methods in industry, not limited to the well-known success stories from the safety-critical domain, like railways and other transportation systems, but also covering other areas such as lithography manufacturing and cloud security in e-commerce, to name but a few. We also report testimonies from a number of representatives from industry who, either directly or indirectly, use or have used formal methods in their industrial project endeavours. These persons are spread geographically, including Europe, Asia, North and South America, and the involved projects witness the large coverage of applications of formal methods, not limited to the safety-critical domain. We thus make a case for the importance of formal methods, and in particular of the capacity to abstract and mathematical reasoning that are taught as part of any formal methods course. These are fundamental Computer Science skills that graduates should profit from when working as computer scientists in industry, as confirmed by industry representatives.

Path planning and task scheduling are two challenging problems in the design of multiple autonomous agents. Both problems can be solved by the use of exhaustive search techniques such as model checking and algorithmic game theory. However, model checking suffers from the infamous state-space explosion problem that makes it inefficient at solving the problems when the number of agents is large, which is often the case in realistic scenarios. In this paper, we propose a new version of our novel approach called MCRL that integrates model checking and reinforcement learning to alleviate this scalability limitation. We apply this new technique to synthesize path planning and task scheduling strategies for multiple autonomous agents. Our method is capable of handling a larger number of agents if compared to what is feasibly handled by the model-checking technique alone. Additionally, MCRL also guarantees the correctness of the synthesis results via post-verification. The method is implemented in UPPAAL STRATEGO and leverages our tool MALTA for model generation, such that one can use the method with less effort of model construction and higher efficiency of learning than those of the original MCRL. We demonstrate the feasibility of our approach on an industrial case study: an autonomous quarry, and discuss the strengths and weaknesses of the methods.

Many industrial application domains utilize safety-critical systems to implement Programmable Logic Controllers (PLCs) software. These systems typically require a high degree of testing and stringent coverage measurements that can be supported by state-of-the-art automated test generation techniques. However, their limited application to PLCs and corresponding development environments can impact the use of automated test generation. Thus, it is necessary to tailor and validate automated test generation techniques against relevant PLC tools and industrial systems to efficiently understand how to use them in practice. In this paper, we present a framework called PyLC, which handles PLC programs written in the Function Block Diagram and Structured Text languages such that programs can be transformed into Python. To this end, we use PyLC to transform industrial safety-critical programs, showing how our approach can be applied to manually and automatically create tests in the CODESYS development environment. We use behaviour-based, translation rules-based, and coverage-generated tests to validate the PyLC process. Our work shows that the transformation into Python can help bridge the gap between the PLC development tools, Python-based unit testing, and test generation.

ProCom is a new component model for real-time and embedded systems, targeting the domains of vehicular and telecommunication systems. In this paper, we describe how the architectural elements of the ProCom component model have been given a formal semantics. The semantics is given in a small but powerful finite state machine formalism,with notions of urgency, timing, and priorities. By defining the semantics in this way, we (i) provide a rigorous and compact description of the modeling elements of ProCom, (ii) seethe ground for formal analysis using other formalisms, and(iii) provide an intuitive and useful description for both practitioners and researchers. To illustrate the approach,we exemplify with a number of particularly interesting cases, ranging from ports and services to components and component hierarchies.