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Robot testing from a machine learning perspective

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dc.title Robot testing from a machine learning perspective en
dc.contributor.author Yadav, Vinod
dc.contributor.author Botchway, Raphael Kwaku
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Komínková Oplatková, Zuzana
dc.relation.ispartof International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
dc.identifier.isbn 978-1-66544-231-2
dc.date.issued 2021
dc.citation.spage 856
dc.citation.epage 859
dc.event.title 2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
dc.event.location Cape Town
utb.event.state-en South Africa
utb.event.state-cs Jihoafrická republika
dc.event.sdate 2021-12-09
dc.event.edate 2021-12-10
dc.type conferenceObject
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.identifier.doi 10.1109/ICECET52533.2021.9698727
dc.relation.uri https://ieeexplore.ieee.org/document/9698727
dc.subject automation en
dc.subject big data en
dc.subject machine learning en
dc.subject robotic testing en
dc.subject test automation en
dc.description.abstract The need to scale software test automation while managing the test automation process within a reasonable time frame remains a crucial challenge for software development teams (DevOps). Unlike hardware, the software cannot wear out but can fail to satisfy the functional requirements it is supposed to meet due to the defects observed during system operation. In this era of big data, DevOps teams can deliver better and efficient code by utilizing machine learning (ML) to scan their new codes and identify test coverage gaps. This study introduces robot testing and machine learning to manage the test automation process to guarantee software reliability and quality within a reasonable timeframe. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1010942
utb.identifier.obdid 43883402
utb.identifier.scopus 2-s2.0-85127759498
utb.identifier.wok 000814669100144
utb.source d-scopus
dc.date.accessioned 2022-04-14T07:07:43Z
dc.date.available 2022-04-14T07:07:43Z
dc.description.sponsorship Univerzita Tomáše Bati ve Zlíně: IGA/CebiaTech/2021/001
dc.description.sponsorship Internal Grant Agency of the Tomas Bata University in Zlin [IGA/CebiaTech/2021/001]; Faculty of Applied Informatics, Tomas Bata University in Zlin
utb.contributor.internalauthor Yadav, Vinod
utb.contributor.internalauthor Botchway, Raphael Kwaku
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.fulltext.affiliation Vinod Yadav1* Faculty of Applied Informatics Tomas Bata University in Zlin Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic vyadav@utb.cz Raphael Kwaku Botchway2 Faculty of Applied Informatics Tomas Bata University in Zlin Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic botchway@utb.cz Roman Senkerik3 Faculty of Applied Informatics Tomas Bata University in Zlin Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic senkerik@utb.cz Zuzana Oplatkova Kominkova4 Faculty of Applied Informatics Tomas Bata University in Zlin Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic oplatkova@utb.cz
utb.fulltext.dates -
utb.fulltext.references [1] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27. [2] Guo, X. (2021, June). Towards Automated Software Testing with Generative Adversarial Networks. In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and NetworksSupplemental Volume (DSN-S) (pp. 21-22). IEEE. [3] Gojare, S., Joshi, R., & Gaigaware, D. (2015). Analysis and design of selenium webdriver automation testing framework. Procedia Computer Science, 50, 341-346. [4] Beck, K. (2003). Test-driven development: by example. Addison-Wesley Professional. [5] Bihlmaier, A., & Wörn, H. (2014, October). Robot unit testing. In International Conference on Simulation, Modeling, and Programming for Autonomous Robots (pp. 255-266). Springer, Cham. [6] Stouky, A., Jaoujane, B., Daoudi, R., & Chaoui, H. (2017, November). Improving software automation testing using jenkins, and machine learning under big data. In International Conference on Big Data Technologies and Applications (pp. 87-96). Springer, Cham. [7] Marale, P. S., & Chandavale, A. A. (2018). Implementation of REST API Automation for Interaction Center. In Intelligent Computing and Information and Communication (pp. 273-277). Springer, Singapore. [8] Shi, Chunhe, et al. "Machine learning under big data." 6th International Conference on Electronic, Mechanical, Information and Management Society. Atlantis Press, 2016.
utb.fulltext.sponsorship This work was supported by the Internal Grant Agency of the Tomas Bata University in Zlin, under the project number IGA/CebiaTech/2021/001. The resources of A.I. Lab further supported the work at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).
utb.wos.affiliation [Yadav, Vinod; Botchway, Raphael Kwaku; Senkerik, Roman; Kominkova, Zuzana Oplatkova] Tomas Bata Univ Zlin, Fac Appl Informat, Nam TG Masaryka 5555, Zlin 76001, Czech Republic
utb.scopus.affiliation Tomas Bata University in Zlin, Faculty of Applied Informatics, Zlin, 76001, Czech Republic
utb.fulltext.projects IGA/CebiaTech/2021/001
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.ou -
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