• KIPS Transactions on Software and Data Engineering
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• v.8 no.6
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• pp.235-242
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• 2019

This paper proposes a reusable test case extraction technique for modified MATLAB Simulink/Stateflow (SL/SF) model. Creating test cases for complicated SL/SF model like ECU(Electrical Control Unit) of automotive, requires a lot of time and effort. An intuitive way to reduce to create new test cases whenever the model changes, is to reuse some test cases which have been generated for the original model. In this paper, we propose a method to define reusable test cases in SL/SF after defining model behavior and judging model equality by test cases. The proposed technique is evaluated using a commercial automotive controller model.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.293-302
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• 2017

This paper proposes a method that generates test cases from Simulink/Stateflow(SL/SF) using a SMT (Satisfiability Modulo Theory) solver, Yices and information of SL/SF model. The most difficult problem to generate test cases from SL/SF model is to solve reachability problem. In the propose method, Yices and the tables built with the model information are utilized to solve the reachability problem. The method utilizes the SMT model, that is the SL/SF model transformed in Yices. The tables built from SL/SF are used for backward processing of the proposed method and increases test generation efficiency. A commercial refrigerator model and two car ECU (Electrical Control Unit) models are used to evaluate the performance of the proposed algorithm..

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.35-41
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• 2017

This paper presents the development of a monitoring and control system for a vehicle cluster using a model-based design technique. For MBD(model-based design), MATLAB GUI(Graphic User Interface), M programs, simulink, state flow, and tool boxes are used to monitor a number of data such as warning, interrupts, and etc. connected to a real vehicle cluster. As a monitoring tool, a PC(Personal Computer) station interworks with the real vehicle cluster through the interface commands of tool boxes. Thus, unlike existing text-based designs, the MBD based vehicle cluster system provides very easy algorithm updates and addition, since it offers a number of blocks and state flow programs for each functional actions. Furthermore, the proposed MBD technique reduces the required time and cost for the development and modification of a vehicle cluster, because of verification and validation of the cluster algorithm on the monitor through a PC.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.12
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• pp.653-662
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• 2016

This paper describes a test case generation algorithm for Simulink/Stateflow models based on the Rapidly exploring Random Tree (RRT) algorithm that has been successfully applied to path finding. An important factor influencing the performance of the RRT algorithm is the metric used for calculating the distance between the nodes in the RRT space. Since a test case for a Simulink/Stateflow (SL/SF) model is an input sequence to check a specific condition (called a test target in this paper) at a specific status of the model, it is necessary to drive the model to the status before checking the condition. A status maps to a node of the RRT. It is usually necessary to check various conditions at a specific status. For example, when the specific status represents an SL/SF model state from which multiple transitions are made, we must check multiple conditions to measure the transition coverage. We propose a unique distance calculation metric, based on the observation that the test targets are gathered around some specific status such as an SL/SF state, named key nodes in this paper. The proposed metric increases the probability that an RRT is extended from key nodes by imposing penalties to non-key nodes. A test case generation algorithm utilizing the proposed metric is proposed. Three models of Electrical Control Units (ECUs) embedded in a commercial vehicle are used for the performance evaluation. The performances are evaluated in terms of penalties and compared with those of the algorithm using a typical RRT algorithm.