[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

Testing-based formal verification has been proposed to automatically verify whether a software program satisfies the requirements written as a formal specification by exploring program paths. There are two tools that can be used to verify path correctness: Hoare logic or symbolic execution. In this document, we will discuss their merits and demerits, as well as the future research directions.

View full abstract

Since neural networks are widely used, how to ensure the reliability of neural networks has become a hot research topic. The first difficulty is that it is difficult to give the pre-condition and post-condition for neural networks, and the second difficulty also exists in traditional software, i.e., the problem of exploding execution paths. Fortunately, the output range of a neural network is easier to give and when an input is given, we can obtain the activation order of neurons in the neural network. Based on the above facts, we propose DeepTBFV, a method for pre-trained neural networks, which uses a testing-based formal verification algorithm to derive the pre-condition of the neural network on a specified path by post-condition. The purpose is to verify and explain the behavior of the neural network.

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract

[in Japanese]

View full abstract