Optimal Sequential-Parallel Test Strategy Generation Method for Complex Systems
针对复杂系统测试资源有限、序贯测试耗时长的问题，提出全局序贯与局部并行结合的新测试模式，并利用神经网络智能算法生成最优策略，仿真显示测试时间最多可减少39.5%。
One of the core tasks of design for testability (DFT) is to generate an optimal test strategy based on the test mode, to isolate faults quickly and accurately. There are currently two modes: 1) sequential test mode (STM) and 2) parallel test mode (PTM). For complex systems, limited testing resources are difficult to meet parallel test conditions, so STM is mostly used. The multisignal flow graph is a widely used model for generating optimal sequential test strategy (STS) in DFT. However, this STM-based model overlooks the possibility of conducting some tests in parallel, resulting in lengthy test time and greatly affecting the reliability and security of the systems. To solve this problem, an optimal sequential-parallel test strategy (SPTS) generation method is proposed. First, a new test mode of global sequential testing and local parallel testing is proposed to generalize the original model. Second, to overcome the combinatorial explosion caused by the new model, we approximate the discrete model to continuous and derive a probability heuristic function. Then, a neural network-intelligent algorithm structure is established to simplify the complex recursion of the heuristic function. Finally, this heuristic function is used to guide the generation of SPTS, which has a shorter test time than STS. Simulation results show that the reduction in time is related to the type and number of locally parallel tests, and reaches 39.5% in a real case.