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Communication Papers of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 37

A machine learning approach for automatic testing

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DOI: http://dx.doi.org/10.15439/2023F4426

Citation: Communication Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 37, pages 215220 ()

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Abstract. Systems' complexity has exponentially increased in recent years. Security and safety have become crucial in critical systems, and end-users now demand clear traceability to ensure protection against errors and external attacks. Meeting this requirement necessitates significant effort in testing. Although automated test sequences can handle a large portion of testing, it is crucial to identify as many errors as possible within the initial hours or days of the testing period. This paper introduces a machine learning-based solution that utilizes learned patterns to determine the test order. It analyzes which functionalities are more susceptible to errors and recursively generates the test sequence to be executed at each step.


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