A Method for Determining whether a Simulink Model is Ready for Test Generation

• Item type: Symposium(1)
• 公開日: 2021-03-15
• タイトル: A Method for Determining whether a Simulink Model is Ready for Test Generation
• タイトル / 言語: en
• タイトル: A Method for Determining whether a Simulink Model is Ready for Test Generation
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: Kyushu University
• 著者所属: Kyushu University
• 著者所属: Kyushu University
• 著者所属(英): en
• 著者所属(英): Kyushu University
• 著者所属(英): en
• 著者所属(英): Kyushu University
• 著者所属(英): en
• 著者所属(英): Kyushu University
• 著者名: Takuya, Ogata; Yuge, Liu; Kenji, Hisazumi
• 著者名(英): Takuya, Ogata; Yuge, Liu; Kenji, Hisazumi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Model-based development (MBD), in which development specifications are written in Simulink, is widely used in the development of embedded control systems. Automatic test generation tools are used to reduce the effort of creating test cases. However, depending on how the model is written, automated generation tools may fail, and it takes time to determine generation failure. In this paper, we propose a method to predict the feasibility of the model test case generation. Specifically, we evaluate the validity of feature generation using the bag of nodes representation of our method and summary statistics of the graphs. The results show that although the AUC of 0.628 is not practically accurate, the initial results using large amounts of data are promising.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Model-based development (MBD), in which development specifications are written in Simulink, is widely used in the development of embedded control systems. Automatic test generation tools are used to reduce the effort of creating test cases. However, depending on how the model is written, automated generation tools may fail, and it takes time to determine generation failure. In this paper, we propose a method to predict the feasibility of the model test case generation. Specifically, we evaluate the validity of feature generation using the bag of nodes representation of our method and summary statistics of the graphs. The results show that although the AUC of 0.628 is not practically accurate, the initial results using large amounts of data are promising.
• 書誌情報: Proceedings of Asia Pacific Conference on Robot IoT System Development and Platform; 巻 2020, p. 77-78, 発行日 2021-03-15
• 出版者 / 言語: ja
• 出版者: 情報処理学会