Cyber-Physical Systems (CPS) confront significant challenges in the assessment of state after experiencing disturbances or attacks, attributed to their inherent complexity. This situation demands comprehensive and expensive experiments for evaluation. Employing black-box optimization methods to optimize test data generation proves efficacious. Nevertheless, prevailing black-box optimization techniques often prioritize trade-offs among objectives, neglecting the search space’s multimodality. To bridge this divide, we draw inspiration from multi-objective multimodal optimization problems (MMOPs) to address black-box optimization problems, proposing a multimodal multi-objective test data generation method (MMOTDG) for testing the state of CPS under disturbances and attacks. The clustering-based particle swarm optimization leveraging adaptive resonance theory, termed CARTPSO, is employed to solve MMOPs in the test data generation process. Experiment results demonstrate that CARTPSO shows significantly superior performance to five leading multimodal multi-objective algorithms across 11 benchmark functions. A novelty co-simulation testing environment is built for testing the state of aircraft encountering wind disturbance in a black-box manner. The proposed MMO-TDG is applied in this environment to generate test data against random search and NSGAII-based test data generation method. Results show that test data generated by MMO-TDG not only exhibit diversity but also effectively fulfill the testing objectives.

The prediction for software bug number provides vital guidance to the quality management and software testing. In this paper, a novel software bug number prediction method was proposed based on complex network considering control flow. Firstly, for each release of software, we constructed the Call Graph (CG), and for each release, Control Flow Graph (CFG) of every function were constructed. Then the CG Metrics (CGM) and CFG Metrics (CFGM) for each version were calculated with indicators from complex-network science. Finally, the results were sent to Panel Data Model (PDM) to perform the prediction on bugs fixed number. The experimental result showed that our method outperformed other prediction methods by 9.35% to 16.85%, and introducing CFGM reduced MAE by 5.1% to 27.8% than barely use CGM. The prediction of fixed bugs could indicate the software quality, and assist the quality control of software engineering.

As a safety-critical system, the reliable operation of smart grids is crucial to economic prosperity and social stability, and reliability analysis and evaluation is one of the effective means of achieving this goal. With smart grids continue to grow in size and complexity, complex network analysis are of useful to understand salient properties of complex systems by modeling smart grids as network system. In light of this, many studies analyze the reliable degree of smart grid from multiple scales reliability, vulnerability, resilience, stability, robustness, survivability, etc. Whereas, those concepts are both different and similar to each other, which tend to be confusing for beginners. This paper holds that reliability, vulnerability and resilience are three important concepts which can representatively describe the reliability level of network of smart grid during and after perturbation, and aims to provide a focused overview of complex networks-based reliability, vulnerability, and resilience analysis for smart grid. We hope this survey will bridge academic researchers and industry engineers in adopting appropriate issues for possibly depth future cooperation.

The flight reliability has been receiving considerable attention. However, the ability of the aircraft recovers to normal flight state from a perturbation were not considered under most circumstances. In this study, a simulation based intelligent analysis framework is proposed to identify the reliability, resilience and vulnerability states of Boeing 737 MAX aircraft disturbed by Maneuvering Characteristics Augmentation System (MCAS) system abnormal activation during the flight. Multiswarm particle swarm optimization (multiswarm PSO) algorithm based test cases generation strategy, aircraft failure behavior model which reflects aerodynamics of the aircraft after the horizontal stabilizer deflection caused by MCAS abnormal activation, JSBSim and FlightGear based co-simulation with aerodynamic and visual characteristics and neural network based flight states identification method constitute the proposed framework. Study results show that the proposed method can cover the margin of resilience and vulnerability quickly and the classification model can identify aircraft flight reliability, resilience and vulnerability states corresponding to different inputs accurately. The proposed framework can be used to validate the flight reliability and system resilience in a more efficient way.