• Title/Summary/Keyword: Black-box Testing

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Generating Test Cases of Stateflow Model Using Extended RRT Method Based on Test Goal (테스트 목표 기반의 향상된 RRT 확장 기법을 이용한 Stateflow 모델 테스트 케이스 생성)

  • Park, Hyeon Sang;Choi, Kyung Hee;Chung, Ki Hyun
    • KIPS Transactions on Software and Data Engineering
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    • v.2 no.11
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    • pp.765-778
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    • 2013
  • This paper proposes a test case generation method for Stateflow model using the extended RRT method. The RRT method which has been popularly used for planning paths for complex systems also shows a good performance for test case generation. However, it does not consider the test coverage which is important for test case generation. The proposed extension method hires the concept of test goal achievement to increase test coverage and drives RRT extension in the direction that increases the goal achievement. Considering the concept, a RRT distance metric, random node generation method and modified RRT extension algorithm are proposed. The effectiveness of proposed algorithm is compared with that of the typical RRT algorithm through the experiment using the practical automotive ECUs.

Generating Test Cases of Simulink/Stateflow Model Based on RRT Algorithm Using Heuristic Input Analysis (휴리스틱 입력 분석을 이용한 RRT 기반의 Simulink/Stateflow 모델 테스트 케이스 생성 기법)

  • Park, Hyeon Sang;Choi, Kyung Hee;Chung, Ki Hyun
    • KIPS Transactions on Software and Data Engineering
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    • v.2 no.12
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    • pp.829-840
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    • 2013
  • This paper proposes a modified RRT (Rapidly exploring Random Tree) algorithm utilizing a heuristic input analysis and suggests a test case generation method from Simulink/Stateflow model using the proposed RRT algorithm. Though the typical RRT algorithm is an efficient method to solve the reachability problem to definitely be resolved for generating test cases of model in a black box manner, it has a drawback, an inefficiency of test case generation that comes from generating random inputs without considering the internal states and the test targets of model. The proposed test case generation method increases efficiency of test case generation by analyzing the test targets to be satisfied at the current state and heuristically deciding the inputs of model based on the analysis during expanding an RRT, while maintaining the merit of RRT algorithm. The proposed method is evaluated with the models of ECUs embedded in a commercial passenger's car. The performance is compared with that of the typical RRT algorithm.

Implementation Wireless Internet Security Connection System Using Bluetooth Beacon in Smart Factory (블루투스 비컨을 사용한 스마트 팩토리에서의 무선인터넷 보안 연결 시스템 구현)

  • Jang, Yun Seong;Shin, Soo Young
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.22 no.12
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    • pp.1705-1713
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    • 2018
  • It is currently undergoing the fourth industrial revolution, which is the convergence of ICT and manufacturing, connecting both industrial equipment and production processes to one network and communicating with each other. The fact that they are connected to one network has the advantage of management, but there is a risk of security. In particular, Wi-Fi can be easily accessed by outsiders through a software change of the MAC address or password exposures. In this paper, by applying the method of Beacon using a Bluetooth Low Energy Add in Bluetooth 4.0, we propose a system of black-box approach to secure connections to wireless Internet, users do not have to know the password. We also implemented the proposed system using the raspberry pi and verified the effectiveness of a real-time system by testing the communication.

A Study on the Improvement of Injection Molding Process Using CAE and Decision-tree (CAE와 Decision-tree를 이용한 사출성형 공정개선에 관한 연구)

  • Hwang, Soonhwan;Han, Seong-Ryeol;Lee, Hoojin
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.22 no.4
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    • pp.580-586
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    • 2021
  • The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.