Test optimization selecting method considering key failures

Abstract

The invention discloses a test optimization selecting method considering key failures. The method includes the steps of obtaining a failure-test relevance matrix, establishing an optimization model, solving the optimization model and the like. According to the method, from the angle of system security, the importance of the key failures is analyzed; then based on the relevance matrix, with the test price minimization as the optimization target, with the failure detection rate and isolation rate and the key failure detection rate and isolation rate as the constraints, the test optimization selecting model considering the key failures is established; finally, solution is conducted by means of a binary particle swarm algorithm based on mass center improvement and inertia weight self-adaptiveadjustment. The severe threats caused by key failure detection omission to device security can be effectively eliminated.

Description

technical field [0001] The invention belongs to the technical field of testing and fault diagnosis, and relates to a test optimization selection method considering key faults. Background technique [0002] With the continuous improvement of the complexity of electronic equipment, problems such as poor testability, high test cost and difficult test diagnosis are gradually exposed in use. An important task of test diagnosis is test optimization selection, that is, to select a set of test sets that meet the testability parameter index requirements in the set of all possible tests in the system, and at the same time, the test cost is the smallest. The test optimization selection problem is a typical set covering and combinatorial optimization problem, which is difficult to solve. Commonly used greedy algorithms, AND-OR graph search algorithms, and AO* methods have problems such as high computational complexity, local convergence, and combinatorial explosion, making it difficult...

AI Technical Summary

Problems solved by technology

Although the optimal solution searched by the above algorithm can minimize the test cost and meet the testability index requirements, the abandonment of some tests may cause some critical faults with a small probability to be unable to be detected and isolated, thereby affecting the safe operation and operation of the system. Subsequent maintenance poses a serious burden

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