The integration of combinatorial testing (CT) with safety requirement analysis remains challenging for complex software systems, particularly when evolving requirements necessitate dynamic updates to test constraints. Traditional CT methods often fail to systematically incorporate safety constraints derived from early-phase requirements, leading to test suites that become obsolete as system specifications change. In this paper, we propose a novel method that embeds system-theoretic process analysis (STPA) into the CT workflow, enabling the automatic derivation and maintenance of safety-aware combinatorial test. Our approach First formalizes STPA-identified unsafe control actions and hazard mitigation rules as refined constraints, then integrated the refined constraints into constraint model to guide incremental test sequence generation. Experimental results demonstrate that our method achieves an effective improvement in test generation efficiency across three representative case studies (robotic control, secure vault system and civil aircraft flight mode transitions), while maintaining compliance with dynamically evolving safety requirements.

Summary Collaborative and autonomous driving vehicles combine hardware and software complex processes, also are heavily dependent on and influenced by the world of physical and cyber interactions. They have enabled many new features and advanced functionalities, such as stochastic and hybrid natures, mobile spatial topologies, and time‐critical dependability. However, the existing modeling and verification techniques have not established faith in proving correctness and safety. Spatial and time collision avoidance remains crucial obstacles on the path to becoming ubiquitous and dependable. In order to ensure safety, we first design an accident prediction architecture in system design‐time and run‐time stages. We apply it on collaborative and autonomous overtaking systems involving spatial‐ and time‐critical accident predictions. Then, we develop a novel and dedicated spatio‐clock stochastic specification language (SCSSL) to describe safety invariants and guards in domain‐specific autonomous driving systems. Next, we create the spatio‐clock stochastic and hybrid automata models based on SCSSL in order to model inherently stochastic and hybrid behaviors. To illustrate the effectiveness of spatio‐clock consistency stochastic specification and verification, we adopt statistical model checking natively to provide reliable predictions for the incoming collision instants and positions. Finally, we present an illustrative overtaking case study to verify spatio‐clock stochastic and hybrid related properties and ensure correct modeling, and demonstrate the significance of our proposed approach.

Safety-critical cyber-physical systems (SC-CPS) have the characteristics of distributed, heterogeneous, strong coupling of computing resources and physical resources. With the increased acceptance of Model-Driven Development (MDD) in the safety-critical domain, the SysML language has been broadly used. Increasing complexity results in the formal verification of the SysML models of SC-CPS often faces the so-called state-explosion problem. Moreover, safety analysis is also an important step to ensure the quality of SC-CPS. Thus, this article proposes an integrated SysML modelling and verification approach to cover specification of nominal behaviour and safety. First, an extension of SysML is presented, in which the contract information (i.e. Assume and Guarantee) is extended for SysML block diagrams and a Safety Profile is proposed to describe safety-related concepts. Second, the transformation from SysML to the compositional verification tool OCRA is given. Third, the safety analysis is achieved by translating the Safety Profile model into FTA (Fault Tree Analysis). Finally, the prototype tools including SysML2OCRA and SafetyProfile2FTA are represented, and the effectiveness of the method proposed in this paper is verified through actual industrial cases.

Safety-Critical Cyber-Physical System (SCCPS) refers to the system that if the system fails or its key functions fail, it will cause casualties, property damage, environmental damage, and other catastrophic consequences. Therefore, it is vital to verify the safety of safety critical systems. In the community, the SCCPS safety verification mainly relies on the statistical model checking methodology, but for SCCPS with extremely high safety requirements, the statistical model checking method is difficult/infeasible to sample the extremely small probability event since the probability of the system violating the safety is very low (rare property). In response to this problem, we propose a new method of statistical model checking for high-safety SCCPS. Firstly, with the CTMC-approximated SCCPS path probability space model, it leverages the maximum likelihood estimation method to learn the parameters of CTMC. Then, the embedded DTMC can be derived from CTMC, and a cross-entropy optimization model based on DTMC can be constructed. Finally, we propose an algorithm of iteratively learning the optimal importance sampling distribution on the discrete path space and an algorithm to check the statistical model of verifying the rare attribute. Eventually, experimental results show that the method proposed in this paper can effectively verify the rare attributes of SCCPS. Under the same sample size, comparing with the heuristic importance sampling methods, the estimated value of this method can be better distributed around the mean value, and the related standard deviation and relative error are reduced by more than an order of magnitude.