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A method and device for program verification based on reinforcement learning

A program verification and reinforcement learning technology, applied in the field of program verification, can solve the problems of high time cost and low efficiency of abstract domain, and achieve the effect of improving efficiency, improving accuracy, reducing error and reducing speed

Active Publication Date: 2022-07-05
TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method requires a lot of time cost, and the process of determining the abstract domain is inefficient

Method used

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  • A method and device for program verification based on reinforcement learning
  • A method and device for program verification based on reinforcement learning
  • A method and device for program verification based on reinforcement learning

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Embodiment Construction

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0034] It should be noted that the terms "comprising" and "having" and any modifications thereof in the embodiments of the present invention and the accompanying drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the steps or units listed, but optionally also includes steps or units not listed, or optionally also includes For other steps or...

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Abstract

Embodiments of the present invention disclose a method and device for program verification based on reinforcement learning. The method includes: acquiring a program to be verified; the program to be verified includes a plurality of subprograms; running a first subprogram to obtain a corresponding first program feature; taking the next subprogram of the first subprogram as a current subprogram, The program feature is used as the current program feature; the current program feature is input into the neural network model obtained by pre-training, and the current abstract domain corresponding to the current program feature is obtained; based on the current abstract domain, the current subprogram is verified, and the current subprogram corresponding to the current subprogram is obtained. Program features; when there are unverified subprograms, take the next subprogram of the current subprogram as the current subprogram, return to the execution, and input the current program features into the pre-trained neural network model to obtain the current abstraction corresponding to the current program features Domain steps. By applying the solutions provided by the embodiments of the present invention, the efficiency of determining the abstract domain can be improved.

Description

technical field [0001] The present invention relates to the technical field of program verification, in particular, to a method and device for program verification based on reinforcement learning. Background technique [0002] The program is complex and error-prone, and manual testing cannot guarantee the completeness of the test. Therefore, program verification is necessary, especially in fields that are highly sensitive to program security, such as the aerospace field. [0003] CEGAR (Counter-example Guided Abstract Refinement) is an important method in program verification. On the basis of abstract modeling of the program, the technology is based on extracting unreachable errors from the current abstraction level. Paths, also known as counterexample paths, are used to iteratively refine an abstract model from an initial state of high abstraction to a state that approximates the behavior of the real program. [0004] In the framework of CEGAR, when a counterexample path i...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F11/36G06N3/04G06N3/08G06K9/62
CPCG06F11/3612G06F11/3608G06N3/04G06N3/084G06F18/295
Inventor 贺飞杨璨于千山杨柳
Owner TSINGHUA UNIV