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master's thesis
Predviđanje pogrešaka u programskoj podršci pomoću strojnog učenja
2024. urn:nbn:hr:200:276115
Cvitković, Marko
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Programming Languages and Systems

Cite this document

Cvitković, M. (2024). Predviđanje pogrešaka u programskoj podršci pomoću strojnog učenja (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:276115

Cvitković, Marko. "Predviđanje pogrešaka u programskoj podršci pomoću strojnog učenja." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2024. https://urn.nsk.hr/urn:nbn:hr:200:276115

Cvitković, Marko. "Predviđanje pogrešaka u programskoj podršci pomoću strojnog učenja." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2024. https://urn.nsk.hr/urn:nbn:hr:200:276115

Cvitković, M. (2024). 'Predviđanje pogrešaka u programskoj podršci pomoću strojnog učenja', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 18 November 2024, https://urn.nsk.hr/urn:nbn:hr:200:276115

Cvitković M. Predviđanje pogrešaka u programskoj podršci pomoću strojnog učenja [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2024 [cited 2024 November 18] Available at: https://urn.nsk.hr/urn:nbn:hr:200:276115

M. Cvitković, "Predviđanje pogrešaka u programskoj podršci pomoću strojnog učenja", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:200:276115

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Metadata
TitlePredviđanje pogrešaka u programskoj podršci pomoću strojnog učenja
Title (english)Software defect prediction using machine learning
AuthorMarko Cvitković
MentorDražen Bajer (mentor)
MentorMario Dudjak (sumentor)
Committee memberZdravko Krpić (predsjednik povjerenstva)
Committee memberMario Dudjak (član povjerenstva)
Committee memberBruno Zorić (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Programming Languages and Systems)
Osijek
Defense date and country2024-10-04, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
Cilj ovog diplomskog rada je istražiti problem predviđanja pogrešaka u programskoj podršci koristeći različite metode strojnog učenja. Glavni izazov bio je neuravnoteženost klasa unutar korištenih skupova podataka, što je negativno utjecalo na učinkovitost klasifikacijskih modela. U cilju poboljšanja performansi modela, primijenjene su tehnike odabira značajki (Pearsonov koeficijent korelacije, zajednička informacija, ANOVA F-test i slijedna pretraga unaprijed) te metode uzorkovanja (nasumično preuzorkovanje, nasumično poduzorkovanje i SMOTE). Najbolje rezultate postigla je kombinacija NB s tehnikom odabira značajki pomoću omotača slijedne pretrage unaprijed, kao i DT u kombinaciji s nasumičnim preuzorkovanjem. Unatoč tome, pokazalo se da ne postoji univerzalna metoda koja donosi optimalne rezultate za sve skupove podataka, već je potrebno prilagoditi pristup ovisno o specifičnostima problema.
Abstract (english)
The aim of this thesis is to explore the problem of software defect prediction using various machine learning methods. The main challenge was the class imbalance within the datasets used, which negatively impacted the performance of classification models To improve model performance, feature selection techniques (Pearson correlation coefficient, mutual information, ANOVA F-test, and sequential forward selection) and sampling methods (random oversampling, random undersampling, and SMOTE) were applied. The best results were achieved by combining Naive Bayes with the sequential forward selection wrapper feature selection method, as well as decision trees combined with random oversampling. However, it was found that no universal method guarantees optimal results for all datasets, and the approach must be adapted based on the specific characteristics of the problem.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:276115
Study programmeTitle: Graduate University Study Programme in Computer Engineering
Study programme type: university
Study level: graduate
Academic / professional title: sveučilišni/a magistar/magistra inženjer/inženjerka računarstva (sveučilišni/a magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-10-16 07:39:53