公开(公告)号：US20210001870A1

公开(公告)日：2021-01-07

申请号：US16908307

申请日：2020-06-22

Applicant: HYUNDAI MOTOR COMPANY ,  KIA MOTORS CORPORATION ,  Ewha University - Industry Collaboration Foundation

Inventor： Hoon JANG ,  Hyeon A CHAE ,  Byoung Ju CHOI

IPC: B60W50/02 ,  G06F11/36

Abstract: A test apparatus for generating a test case based on a fault injection technique and a method of controlling the same are disclosed. The method includes identifying at least one function in a program to be tested based on a software detailed design, generating a test design document based on fault location that can be generated in connection with the identified at least one function and a fault type to be injected into the fault location, searching for the fault location to be injected based on the generated test design document and source code of the program, determining a fault injection scheme and the fault type, and predicting a result by applying a fault injection corresponding to the fault injection scheme and the fault type into the searched location to generate a test case.

公开(公告)号：US20240202099A1

公开(公告)日：2024-06-20

申请号：US18368833

申请日：2023-09-15

Applicant: EWHA UNIVERSITY-INDUSTRY COLLABORATION FOUNDATION

Inventor： Byoung Ju CHOI ,  Ji Hyun PARK

IPC: G06F11/36

CPC classification number: G06F11/3612 ,  G06F11/3624 ,  G06F11/3636

Abstract: Provided are a device and method for detecting a variable vulnerability in software using a machine learning (ML) model. The method performed by an analysis device includes receiving a source code of a program to be analyzed, replacing call functions, variable names, and call stack functions in an execution log generated during execution of the source code with certain identifiers (IDs) to preprocess the execution log, analyzing the preprocessed execution log through a pretrained first learning model to classify whether each pair of a global variable and a call function is at an initialization location, analyzing the preprocessed execution log through a pretrained second learning model to estimate a maximum value and a minimum value of the global variable, and determining whether the global variable is vulnerable on the basis of information output by the first learning model and information output by the second learning model.