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Title:Zaznavanje in napovedovanje prisotnosti napak v izvorni kodi s pomočjo metrik programske opreme in strojnega učenja
Authors:Polanec, Mihael (Author)
Kokol, Peter (Mentor) More about this mentor... New window
Files:.pdf MAG_Polanec_Mihael_2018.pdf (2,82 MB)
.zip MAG_Polanec_Mihael_2018.zip (3,20 MB)
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrski nalogi smo spoznali različne tipe metrik za merjenje karakteristik izvorne kode in algoritme strojnega učenja. Obe področji smo združili v aplikaciji, s katero smo testirali natančnost napovedovanja prisotnosti napak v izvorni kodi z različnimi algoritmi strojnega učenja. Aplikacija je razvita v Javi s pomočjo knjižnice WEKA 3.8. S pridobljenimi rezultati smo pokazali, da bi nekatere pristope lahko uporabili za napovedovanje napak v izvorni kodi.
Keywords:metrike programske opreme, strojno učenje, napake programske opreme
Year of publishing:2018
Publisher:M. Polanec
COBISS_ID:21989654 Link is opened in a new window
License:CC BY-NC-ND 4.0
This work is available under this license: Creative Commons Attribution Non-Commercial No Derivatives 4.0 International
Categories:KTFMB - FERI
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Secondary language

Title:Fault presence detection and prediction in the source code using software metrics and machine learning
Abstract:In this master thesis we studied various types of metrics for measuring source code characteristic and machine learning algorithms. We combined the two fields in an application to test the accuracy of fault presence detection with various machine learning algorithms. The application was developed in Java using the WEKA 3.8 library. Using the btained results, we have shown that some approaches could be used to predict errors in the source code.
Keywords:software metrics, machine learning, software faults


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