Reinforcement learning-based Simulink software testing method

Abstract

The invention discloses a reinforcement learning-based Simulink software testing method. The reinforcement learning-based Simulink software testing method comprises two parts: a use case generation part and a use case testing part, the case generation part comprises the following steps: (1) selecting an initial model in a test case library, (2) inputting the state characteristics of the initial model into a reinforcement learning agent, (3) selecting the next action to be executed by the model in an action library by the agent according to the input, and (4) outputting an action index to the model, and executing the action by the model. A case test part: (5) carrying out compiling test on the model after action execution by MATLAB, (6) repairing compiling errors if compiling is not passed, (7) carrying out differential test on the model after compiling is passed, (8) judging whether a test result is equivalent in function or not, if the test result is equivalent, considering that no bug is found, if a difference exists, considering that the bug is found, and (9) based on the test result, judging that the bug is not found. And updating the reinforcement learning agent, so that the reinforcement learning agent tends to generate a model of the easily triggered bug.

Description

technical field [0001] The invention relates to the field of software testing, in particular to a Simulink software testing method based on reinforcement learning. Background technique [0002] CPS (Cyber-Physical System) is a multi-dimensional complex system that realizes the integrated design of computing, communication and physical systems. It is widely used in the design and simulation process of safety-critical fields such as automobiles and aerospace. Bugs are critical. The current bug testing work for the CPS toolchain, whether by building a new CPS model or by mutating an existing CPS model, aims to generate a model that puts pressure on the compiler. [0003] For example, the test of Simulink of MathWorks, the most commonly used development tool for CPS, focuses on its method of building a model. The related work of this method is divided into two parts. One part is SLforge in the literature [1], which randomly selects a certain model through the roulette algorithm...

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Problems solved by technology

However, in the above methods, due to the lack of guidance of the random generation method, a certain probability will cause the generated model to fail to compile, and frequent iterations to repair errors greatly reduce the efficiency of compiler testing, while the deep learning method is used to train a network to guide the method of model generation. , requires a large number of real models as training data, but there are not so many real models in reality, resulting in insufficient learned information to generate an ideal network, and it is difficult to generate an ideal test model

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