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Title:Primerjalna analiza metod testiranja informacijskih rešitev z inteligentnimi komponentami : magistrsko delo
Authors:ID Zakelšek, Haidi (Author)
ID Šumak, Boštjan (Mentor) More about this mentor... New window
Files:.pdf MAG_Zakelsek_Haidi_2026.pdf (2,53 MB)
MD5: D278B8E0CF1B59766F41E194B50EA0B2
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Razvoj inteligentnih sistemov prinaša nove izzive pri testiranju in zagotavljanju njihove kakovosti, saj rezultati temeljijo na verjetnosti namesto na vnaprej določenih izidih. Magistrsko delo obravnava problem testiranja sistemov z inteligentnimi komponentami, pri čemer smo sistematično pregledali obstoječe metode in ogrodja ter izvedli primerjalno analizo metamorfnega, diferencialnega in testiranja na podlagi lastnosti. Cilj naloge je bil ugotoviti, kako se izbrane metode razlikujejo glede na vrsto zaznanih odstopanj in ali njihova kombinacija omogoča širši vpogled v delovanje sistema. V eksperimentu smo vse tri metode implementirali na konvolucijski nevronski mreži, naučeni na javno dostopnem podatkovnem naboru. Rezultate smo primerjali s Kruskal-Wallisovim testom in post-hoc analizo. Ugotovili smo, da metode odkrivajo različne tipe nepravilnosti in imajo različne prednosti in omejitve. Rezultati so pokazali statistično značilne razlike med izbranimi pristopi in potrjujejo njihovo uporabnost pri zagotavljanju kakovosti inteligentnih informacijskih rešitev.
Keywords:testiranje inteligentnih komponent, testiranje sistemov z inteligentno komponento, inteligentna komponenta, metamorfno testiranje, testiranje na podlagi lastnosti, diferencialno testiranje.
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[H. Zakelšek]
Year of publishing:2026
Number of pages:1 spletni vir (1 datoteka PDF (…,… str.))
PID:20.500.12556/DKUM-98427 New window
UDC:004.89(043.2)
COBISS.SI-ID:283573507 New window
Publication date in DKUM:29.06.2026
Views:256
Downloads:25
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-ND 4.0, Creative Commons Attribution-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nd/4.0/
Description:Under the NoDerivatives Creative Commons license one can take a work released under this license and re-distribute it, but it cannot be shared with others in adapted form, and credit must be provided to the author.
Licensing start date:11.06.2026

Secondary language

Language:English
Title:Comparative analysis of testing methods for information systems with intelligent components
Abstract:The development of intelligent systems introduces new challenges in testing and quality assurance, as results are based on probability rather than predefined outcomes. This master's thesis addresses the problem of testing systems with intelligent components, systematically reviewing existing methods and frameworks and conducting a comparative analysis of metamorphic, differential, and property-based testing. The aim of the thesis was to determine how the selected methods differ in terms of the types of deviations they detect and whether their combination enables a broader insight into system behaviour. In the experiment, all three methods were implemented on a convolutional neural network trained on a publicly available dataset. The results were compared using the Kruskal-Wallis test and post-hoc analysis. We found that the methods detect different types of irregularities and have different strengths and limitations. The results showed statistically significant differences between the selected approaches and confirm their practical applicability in ensuring the quality of intelligent information solutions.
Keywords:intelligent components testing, testing systems with intelligent component, intelligent component, metamorphic testing, property based testing, differential testing.


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