Method for generating simplified test case set of flight control airborne model

Abstract

The invention discloses a method for generating a simplified test case set of a flight control airborne model, and belongs to the technical field of aviation airborne model testing. The method comprises the following steps of: firstly loading the flight control airborne model, acquiring information of each input parameter, and acquiring an initial data set of each input parameter after valuing the parameters; and then generating uncovered combination pairs combined pairwise, acquiring the initialization position of each longhorn beetle, sequentially acquiring candidate test cases corresponding to each input parameter by adopting a longhorn beetle algorithm according to the acquiring sequence of the input parameters, generating N candidate test cases, selecting the candidate test case with the maximum effective coverage combination number in the test case set, adding the candidate test case into the test case set, deleting the combinations covered by the test cases in the uncovered combination pairs until the combinations are zero, and finally acquiring the simplified test case set. According to the method, a fitness function suitable for generation of the test cases in pairwise combination is constructed, the candidate test cases are generated in a mode of the longhorn beetle swarm, and then the optimal test case is selected, so that the diversity and the simplification of the test cases are improved.

Description

technical field [0001] The invention belongs to the technical field of aviation airborne model testing, and in particular relates to a method for generating a condensed test case set of an airborne model of flight control. Background technique [0002] With the continuous development of technology, the application of Unmanned Aerial Vehicle (UAV) is becoming more and more extensive. The flight control airborne model is the core of the UAV software system, and safe and stable operation is the basic guarantee for UAVs to complete complex tasks. The flight control airborne model has stricter requirements on safety, stability and reliability than other common models. The test of the flight control airborne model is an extremely important link in the development and one of the main means to ensure the development quality. The workload of flight control testing is huge, generally occupying 40% to 50% of the entire development process, including compatibility testing, logic testin...

AI Technical Summary

Problems solved by technology

[0004] The random method is to randomly select the values ​​of the model parameters, and randomly generate test cases that meet the value requirements, but it cannot fully meet the requirements for the flight control airborne model with strict test requirements; the representative numerical method is the orthogonal The Latin method generates test cases by constructing a Latin matrix, but it has certain requirements on the number of flight control parameters and the number of values, which has relatively large limitations; the greedy algorithm is the most widely used method in the generation of combined test cases, which can be divided into The main principle of one-dimensional expansion and two-dimensional expansion is to take the local optimum and approximate the global optimum. This approximation can significantly improve the efficiency of the algorithm, but it is easy to generate redundancy, generate a large number of test cases, and increase the follow-up testers. The analysis time of the test; the heuristic search algorithm uses the search ability of the heuristic intelligent algorithm to find the optimal combination of test cases in the uncovered combination set, which can generate test cases more accurately, greatly improving the simplification of test cases and reducing test cycle

[0005] The representative algorithms in the heuristic search algorithm include genetic algorithm, ant colony algorithm, particle swarm algorithm, etc. These algorithms can generate relatively streamlined test cases, but there are still shortcomings such as easy to fall into local optimum and difficulty in parameter selection.

As a new heuristic search algorithm, the beetle whisker algorithm has been widely used in path optimization and power scheduling because of its advantages of easy implementation, fast convergence speed and high convergence quality. The algorithm has not been widely used in test case generation, which has research and improvement significance

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