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master's thesis
Predviđanje programskih neispravnosti zasnovano na metodi izračuna prihvatljive razine rizika
2018. urn:nbn:hr:190:472600
Mohović, Marino
University of Rijeka
Faculty of Engineering
Department of Computer Engineering
Section of Software Engineering

Cite this document

Mohović, M. (2018). Predviđanje programskih neispravnosti zasnovano na metodi izračuna prihvatljive razine rizika (Master's thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:472600

Mohović, Marino. "Predviđanje programskih neispravnosti zasnovano na metodi izračuna prihvatljive razine rizika." Master's thesis, University of Rijeka, Faculty of Engineering, 2018. https://urn.nsk.hr/urn:nbn:hr:190:472600

Mohović, Marino. "Predviđanje programskih neispravnosti zasnovano na metodi izračuna prihvatljive razine rizika." Master's thesis, University of Rijeka, Faculty of Engineering, 2018. https://urn.nsk.hr/urn:nbn:hr:190:472600

Mohović, M. (2018). 'Predviđanje programskih neispravnosti zasnovano na metodi izračuna prihvatljive razine rizika', Master's thesis, University of Rijeka, Faculty of Engineering, accessed 31 March 2025, https://urn.nsk.hr/urn:nbn:hr:190:472600

Mohović M. Predviđanje programskih neispravnosti zasnovano na metodi izračuna prihvatljive razine rizika [Master's thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2018 [cited 2025 March 31] Available at: https://urn.nsk.hr/urn:nbn:hr:190:472600

M. Mohović, "Predviđanje programskih neispravnosti zasnovano na metodi izračuna prihvatljive razine rizika", Master's thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:190:472600

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Metadata
TitlePredviđanje programskih neispravnosti zasnovano na metodi izračuna prihvatljive razine rizika
Title (english)Software Defect Prediction Based on the Value of an Acceptable Risk Level Method
AuthorMarino Mohović
MentorTihana Galinac Grbac (mentor)
Committee memberKristijan Lenac (predsjednik povjerenstva)
Committee memberIvo Ipšić (član povjerenstva)
Committee memberGoran Mauša (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
(Department of Computer Engineering)
(Section of Software Engineering)
Rijeka
Defense date and country2018-09-27, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
The knowledge about the software metrics, which serve as quality indicators, is vital for the efficient allocation of resources in quality assurance activities. Recent studies showed that some software metrics exhibit threshold effects and can be used for software defect prediction. Our goal was to analyze if the threshold derivation process could be used to improve a standard classification models for software defect prediction, rather than to search for universal threshold values. We proposed two... More classification models based on Bender method for threshold derivation to test this idea, named Threshold Naive Bayes and Threshold Voting. Threshold Naive Bayes is a probabilistic classifier based on Naive Bayes and improved by threshold derivation. Threshold Voting is a simple type of ensemble classifier which is based solely on threshold derivation. The proposed models were tested in a case study based on datasets from subsequent releases of large open source projects and compared against the standard Naive Bayes classifier in terms of geometric mean (GM) between true positive and true negative rate. The results of our case study showed that the Threshold Naive Bayes classifier performs better than the other two when compared in terms of GM. Hence, this study has shown that threshold derivation process for software metrics may be used to improve the performance of standard classifiers in software defect prediction. Future research will analyze its effectiveness in general classification purposes and test on other types of data. Less
Abstract (english)
The knowledge about the software metrics, which serve as quality indicators, is vital for the efficient allocation of resources in quality assurance activities. Recent studies showed that some software metrics exhibit threshold effects and can be used for software defect prediction. Our goal was to analyze if the threshold derivation process could be used to improve a standard classification models for software defect prediction, rather than to search for universal threshold values. We proposed... More two classification models based on Bender method for threshold derivation to test this idea, named Threshold Naive Bayes and Threshold Voting. Threshold Naive Bayes is a probabilistic classifier based on Naive Bayes and improved by threshold derivation. Threshold Voting is a simple type of ensemble classifier which is based solely on threshold derivation. The proposed models were tested in a case study based on datasets from subsequent releases of large open source projects and compared against the standard Naive Bayes classifier in terms of geometric mean (GM) between true positive and true negative rate. The results of our case study showed that the Threshold Naive Bayes classifier performs better than the other two when compared in terms of GM. Hence, this study has shown that threshold derivation process for software metrics may be used to improve the performance of standard classifiers in software defect prediction. Future research will analyze its effectiveness in general classification purposes and test on other types of data. Less
Keywords
Languageenglish
URN:NBNurn:nbn:hr:190:472600
Study programmeTitle: Computer Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2018-10-03 06:12:59
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