Robot testing from a machine learning perspective

Yadav, Vinod; Botchway, Raphael Kwaku; Šenkeřík, Roman; Komínková Oplatková, Zuzana

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	-