Scenario-based techniques, also known as scenario methods, have been actively employed to resolve intricate problems for engineering complex software systems. Scenarios are powerful tools that allow engineers to analyze the dynamics and contexts of complex systems. Despite the widespread use, there is a lack of a well-established reference framework that systematically organizes key concepts and attributes of scenarios. This has left engineers without a systematic guidance at the method level, hindering their ability to utilize the scenario methods effectively. To address the challenges associated with scenario methods, this study aims to provide a reference framework and modeling method. By conducting a literature review and suggesting a Conceptual Scenario Framework (CSF), we establish a conceptual basis that systematically presents the core concepts and characteristics of scenarios. Additionally, we introduce the Extensible Scenario Modeling Method (ESMM) that empowers engineers to perform scenario modeling and domain-specific extensions using the framework. With the inclusion of the Extensible Scenario Modeling Language (ESML), which comprises domain-general model types and classes for scenario description and ontological analysis, ESMM facilitates flexible design of domain-specific scenario elements through language-level extensions. This study assesses the proposed method in comparison to existing scenario development methods in the automated driving system domain. Through an analysis of their ability to represent scenario data, it was established that the language constructs of ESML possess semantic expressiveness suitable for serving as a reference framework. Furthermore, the findings from the case study validate the extensibility of ESMM for specialization in creating a scenario modeling language tailored to specific domains, while also effectively supporting the ontological analysis of particular application domains.

A Cyber-Physical System-of-Systems (CPSoS) has innate uncertainties from operation in the physical environment and interaction among the constituent systems. These uncertainties make a CPSoS more susceptible to the oracle problem, a challenge in determining the correct behavior when testing the system. Metamorphic testing (MT) suggests a solution to addressing this challenge by utilizing metamorphic relations (MRs), relations among multiple inputs and corresponding outputs of the system. However, when applying MT on a CPSoS, generating MRs is difficult due to the continuous operation of a CPSoS in uncertain environment. In this study, we propose a method to automatically generate MRs from field operational test (FOT) data logs of a CPSoS. We define an MR template to capture the CPSoS behaviors. We then apply genetic algorithm to adapt the MR generated by the engineers, and thus improve the testing effectiveness. Our method is validated in a case study of an autonomous robot vehicle. Our results show that the automatically generated MRs capture the behaviors of a CPSoS more realistically than the manually generated MRs. With our method, engineers can obtain CPSoS MRs with minimal manual effort.