prikaz prve stranice dokumenta Software Defect Classification with a Variant of NSGA-II and Simple Voting Strategies
No public access
master's thesis
Software Defect Classification with a Variant of NSGA-II and Simple Voting Strategies
2015. urn:nbn:hr:190:153580
Rubinić, Emil
University of Rijeka
Faculty of Engineering

Cite this document

Rubinić, E. (2015). Software Defect Classification with a Variant of NSGA-II and Simple Voting Strategies (Master's thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:153580

Rubinić, Emil. "Software Defect Classification with a Variant of NSGA-II and Simple Voting Strategies." Master's thesis, University of Rijeka, Faculty of Engineering, 2015. https://urn.nsk.hr/urn:nbn:hr:190:153580

Rubinić, Emil. "Software Defect Classification with a Variant of NSGA-II and Simple Voting Strategies." Master's thesis, University of Rijeka, Faculty of Engineering, 2015. https://urn.nsk.hr/urn:nbn:hr:190:153580

Rubinić, E. (2015). 'Software Defect Classification with a Variant of NSGA-II and Simple Voting Strategies', Master's thesis, University of Rijeka, Faculty of Engineering, accessed 26 December 2024, https://urn.nsk.hr/urn:nbn:hr:190:153580

Rubinić E. Software Defect Classification with a Variant of NSGA-II and Simple Voting Strategies [Master's thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2015 [cited 2024 December 26] Available at: https://urn.nsk.hr/urn:nbn:hr:190:153580

E. Rubinić, "Software Defect Classification with a Variant of NSGA-II and Simple Voting Strategies", Master's thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2015. Available at: https://urn.nsk.hr/urn:nbn:hr:190:153580

Please login to the repository to save this object to your list.
Metadata
TitleSoftware Defect Classification with a Variant of NSGA-II and Simple Voting Strategies
Title (croatian)PREDIKCIJA SOFTVERSKIH POGREŠAKA UPOTREBOM SKUPNIH METODA
AuthorEmil Rubinić
MentorTihana Galinac Grbac (mentor)
Committee memberŽeljko Jeričević
Committee memberKristijan Lenac
Committee memberTihana Galinac Grbac
GranterUniversity of Rijeka
Faculty of Engineering
Rijeka
Defense date and country2015-09-21, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Program Engineering
Abstract
Software Defect Prediction is based on datasets that are imbalanced and therefore limit the use of machine learning based classification. Ensembles of genetic classifiers indicate good performance and provide a promising solution to this problem. To further examine this solution, we performed additional experiments in that direction. In this paper we report preliminary results obtained by using a Matlab variant of NSGA-II in combination with four simple voting strategies on three subsequent releases of the Eclipse Plug-in Development Environment (PDE) project. Preliminary results indicate that the voting procedure might influence software defect prediction performances.
Keywords
Languageenglish
URN:NBNurn:nbn:hr:190:153580
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 conditionsClosed access
Terms of useLicence In copyright icon
Created on2016-03-04 07:30:28