A Log-Based Testing Approach for Detecting Faults Caused by Incorrect Assumptions About the Environment-Reference-Cited by-同舟云学术

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A Log-Based Testing Approach for Detecting Faults Caused by Incorrect Assumptions About the Environment

Published:2020-01-01 Issue:1 Volume:E103.D Page:170-173
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

JEONG Sooyong¹,JHA Ajay Kumar¹,SHIN Youngsul¹,LEE Woo Jin¹

Affiliation:

1. Kyungpook National University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E103.D/1/E103.D_2019EDL8149/_pdf

Reference14 articles.

1. [1] C. Ebert and C. Jones, “Embedded software: Facts, figures, and future,” Computer, vol.42, no.4, pp.42-52, June 2009. 10.1109/mc.2009.118

2. [2] Y. Shinyashiki, T. Mise, M. Hashimoto, K. Katamine, N. Ubayashi, and T. Nakatani, “Enhancing the ESIM (Embedded Systems Improving Method) by Combining Information Flow Diagram with Analysis Matrix for Efficient Analysis of Unexpected Obstacles in Embedded Software,” Proc. 14th APSEC, Nagoya, Japan, pp.327-333, Dec. 2007. 10.1109/apsec.2007.49

3. [3] K.D. Müller-Glaser, G. Friek, E. Sax, and M.K. Kühl, “Multiparadigm modeling in embedded systems design,” IEEE Trans. Control Syst. Technol., vol.12, no.2, March 2004.

4. [4] G. Karsai, S. Neema, and D. Sharp, “Model-driven architecture for embedded software: A synopsis and an example,” Sci. Comput. Program., vol.73, no.1, pp.26-38, June 2008. 10.1016/j.scico.2008.05.006

5. [5] F. Siavashi and D. Truscan, “Environment Modeling in Model-Based Testing: Concepts, Prospects and Research Challenges,” Proc. 19th International Conference on Evaluation and Assessment in Software Engineering, pp.30-35, Nanjing, China, April 2015. 10.1145/2745802.2745830

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1. Impact of log parsing on deep learning-based anomaly detection;Empirical Software Engineering;2024-08-17

2. Usage Log-Based Testing of Embedded Software and Identification of Dependencies among Environmental Components;IEICE Transactions on Information and Systems;2021-11-01

3. Self-Claimed Assumptions in Deep Learning Frameworks: An Exploratory Study;Evaluation and Assessment in Software Engineering;2021-06-18

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