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Digital Twins (DTs) serve as the backbone of Industry 4.0, offering virtual representations of actual systems, enabling accurate simulations, analysis, and control. These representations help in predicting system behaviour, facilitating multiple real-time tests, and reducing risks and costs while identifying optimization areas. DTs meld cyber and physical realms, accelerating the design and modelling of sustainable innovations. Despite their potential, the complexity of DTs presents challenges in their industrial application. We sketch here an approach to build an adaptable and trustable framework for building and operating DT systems, which is the basis for the academia-industry project A Digital Twin Framework for Dynamic and Robust Distributed Systems (D-RODS). D-RODS aims to address the challenges above, aiming to advance industrial digitalization and targeting areas like system efficiency, incorporating AI and verification techniques with formal support. 

Several studies have shown Model-based Testing (MBT) as an efficient technique for generating fault-effective test cases. However, the automatic generation of test cases is compromised with redundant test cases providing no additional value to the coverage or fault detection effectiveness while impacting test execution efficiency, especially, in a dynamic development environment where providing timely feedback is crucial. These redundant test cases need to be discarded to minimize the test suite size and their effect on the execution cost and efficiency of a test suite. Reducing a test suite becomes challenging for black box testing at the system level when no information regarding the coverage and fault detection effectiveness of the test suite exists. Hence, in this paper, we have presented a test suite optimization approach leveraging different machine learning algorithms, a greedy algorithm, and a similarity measure. The proposed approach generates a reduced test suite by identifying and eliminating redundant test cases from an MBT-generated test suite while having minimal impact on the fault detection rate. We have also performed a comparative evaluation of the optimized test suites with the MBT-generated and manually created test suites in terms of fault detection effectiveness and test execution efficiency using an industrial case study from Alstom Rail AB, Sweden. The results show a significant reduction of 85% to 92% in the size of the test suite. Moreover, we also found the test execution time of the optimized test suite equivalent to the manually created tests and a fault detection rate within the range of 95% to 100% for all test suites under observation.

Requirements elicitation has since long been recognized as critical to the success of requirements engineering, hence also to the success of systems engineering. Achieving sufficient scope and quality in the requirements elicitation process poses a great challenge, given the limited slices of budget and time available for this relatively sizeable activity. Among all predominant requirements elicitation techniques and approaches, operational scenarios development has a special standing and character. The set of operational scenarios is acknowledged as a constituent deliverable in the requirements engineering process, serving many purposes. Hence, ensuring success in the development of operational scenarios constitutes a consequential area of research. In this paper we present the results from an industrial survey on experienced and presumptive success factors in the development of operational scenarios. The survey was done using a strength-based approach, involving engineers and managers in two organizations developing cyber-physical systems in the transportation and construction equipment businesses. Our results suggest that operational scenarios reusability and a collaborative operational scenarios development environment are two prime areas for success. Our study provides two contributions. First, we provide an account of success factors in the view of practitioners. This is fundamental knowledge, since a successful deployment of any state-of-the-art approach and technology in a systems engineering organization needs to take the views of the practitioners into consideration. Second, the study adds input to the body of knowledge on requirements elicitation, and can thereby help generate suggestions on direction for future work by researchers and developers.

Mission planning for multi-agent autonomous systems aims to generate feasible and optimal mission plans that satisfy the given requirements. In this article, we propose a mission-planning methodology that combines (i) a path-planning algorithm for synthesizing path plans that are safe in environments with complex road conditions, and (ii) a task-scheduling method for synthesizing task plans that schedule the tasks in the right and fastest order, taking into account the planned paths. The task-scheduling method is based on model checking, which provides means of automatically generating task execution orders that satisfy the requirements and ensure the correctness and efficiency of the plans by construction. We implement our approach in a tool named MALTA, which offers a user-friendly GUI for configuring mission requirements,  a module for path planning, an integration with the model checker UPPAAL, and functions for automatic generation of formal models, and parsing of the execution traces of models. Experiments with the tool demonstrate its applicability and performance in various configurations of an industrial case study of an autonomous quarry. We also show the adaptability of our tool by employing it on a special case of the industrial case study.

Combinatorial Testing (CT) and Model-Based Testing (MBT) are two recognized test generation techniques. The evidence of their fault detection effectiveness and comparison with industrial state-of-the-practice is still scarce, more so at the system level for safety-critical systems, such as those found in trains. We use mutation analysis to perform a comparative evaluation of CT, MBT, and industrial manual testing in terms of their fault detection effectiveness using an industrial case study of the safety-critical train control management system. We examine the fault detection rate per mutant and relationship between the mutation scores and structural coverage using Modified Condition Decision Coverage (MC/DC). Our results show that CT 3-ways, CT 4-ways, and MBT provide higher mutation scores. MBT did not perform better in detecting 'Logic Replacement Operator-Improved' mutants when compared with the other techniques, while manual testing struggled to find 'Logic Block Replacement Operator' mutants. None of the test suites were able to find 'Time Block Replacement Operator' mutants. CT 2-ways was found to be the least effective test technique. MBT-generated test suite achieved the highest MC/DC coverage. We also found a generally consistent positive relationship between MC/DC coverage and mutation scores for all test suites.

Regulatory standards for engineering safety-critical systems often demand both traceable requirements and specification-based testing, during development. Requirements are often written in natural language, yet for specification purposes, this may be supplemented by formal or semi-formal descriptions, to increase clarity. However, the choice of notation of the latter is often constrained by the training, skills, and preferences of the designers.

The Easy Approach to Requirements Syntax (EARS) addresses the inherent imprecision of natural language requirements with respect to potential ambiguity and lack of accuracy. This paper investigates requirements specification using EARS, and specification-based testing of embedded software written in the IEC 61131-3 language, a programming standard used for developing Programmable Logic Controllers (PLC). Further, we study, by means of an experiment, how human participants translate natural language requirements into EARS and how they use the latter to test PLC software. We report our observations during the experiments, including the type of EARS patterns participants use to structure natural language requirements and challenges during the specification phase, as well as present the results of testing based on EARS-formalized requirements in real-world industrial settings.

Regulatory standards for engineering safety-critical systems often demand both traceable requirements and specification-based testing, during development. Requirements are often written in natural language, yet for specification purposes, this may be supplemented by formal or semi-formal descriptions, to increase clarity. However, the choice of notation of the latter is often constrained by the training, skills, and preferences of the designers.The Easy Approach to Requirements Syntax (EARS) addresses the inherent imprecision of natural language requirements with respect to potential ambiguity and lack of accuracy. This paper investigates requirement formalization using EARS and specification-based testing of embedded software written in the IEC 61131-3 language, a programming standard used for developing Programmable Logic Controllers (PLC). Further, we investigate, by means of an experiment, how human participants translate natural language requirements into EARS and how they use the latter to test PLC software. We report our observations during the experiments, including the type of EARS patterns participants use to structure natural language requirements and challenges during the specification phase, as well as present the results of testing based on EARS-formalized requirements.

System testing is essential for developing high-quality systems, but the degree of automation in system testing is still low. Therefore, there is high potential for Artificial Intelligence (AI) techniques like machine learning, natural language processing, or search-based optimization to improve the effectiveness and efficiency of system testing. This chapter presents where and how AI techniques can be applied to automate and optimize system testing activities. First, we identified different system testing activities (i.e., test planning and analysis, test design, test execution, and test evaluation) and indicated how AI techniques could be applied to automate and optimize these activities. Furthermore, we presented an industrial case study on test case analysis, where AI techniques are applied to encode and group natural language into clusters of similar test cases for cluster-based test optimization. Finally, we discuss the levels of autonomy of AI in system testing. 

Numerous industrial sectors employ Programmable Logic Controllers (PLC) software to control safety-critical systems. These systems necessitate extensive testing and stringent coverage measurements, which can be facilitated by automated test-generation techniques. Existing such techniques have not been applied to PLC programs, and therefore do not directly support the latter regarding automated test-case generation. To address this deficit, in this work, we introduce PyLC, a tool designed to automate the conversion of PLC programs to Python code, assisted by an existing test generator called Pynguin. Our framework is capable of handling PLC programs written in the Function Block Diagram language. To demonstrate its capabilities, we employ PyLC to transform safety-critical programs from industry and illustrate how our approach can facilitate the manual and automatic creation of tests. Our study highlights the efficacy of leveraging Python as an intermediary language to bridge the gap between PLC development tools, Python-based unit testing, and automated test generation.