An automatic testing method and device

Abstract

The invention discloses an automatic testing method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: collecting decisiontrack data of a tester during testing, and taking the decision track data as an expert strategy; Creating a model of a reward function; taking the expert strategy as an input of an inverse reinforcement learning algorithm to obtain a return coefficient enabling the value of the reward function to be maximum; and according to the reward function corresponding to the maximum return coefficient, calculating a strategy corresponding to the maximum cumulative reward function value so as to carry out an automatic test according to the strategy. According to the implementation mode, the reward function is designed through the inverse reinforcement learning algorithm, then the strategy corresponding to the expert strategy is obtained, automatic testing is carried out according to the strategy, onone hand, testing resources are saved, and on the other hand, the testing depth and the coverage range can be close to the testing effect of a tester.

Description

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AI Technical Summary

Benefits of technology

This technology uses an inverted reinforced learning method that helps create better strategies based upon feedback provided by experts over time. These methods are trained automatically without human interference while still being able to match their performance against real-world scenarios accurately. By gathering coordinate values between each pixel click made during tests, this system learns how well they perform under different conditions like temperature changes, humidity levels, etc., resulting in improved training efficiency compared to traditional approaches such as trial/test cycles.

Problems solved by technology

This patented technical problem addressed by this patents relates to improving the efficiency and accuracy of software testing processes while reducing costs associated with manually-based methods like click buttons and monkeys. However, current solutions require expensive equipment and may only cover limited areas due to their lacked ability to consider how well they perform during different situations. Therefore there needs an improved approach called reinforce learning algorithms (RML).

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