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DKUM
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Title:
Zaznavanje in napovedovanje prisotnosti napak v izvorni kodi s pomočjo metrik programske opreme in strojnega učenja
Authors:
ID
Polanec, Mihael
(
Author
)
ID
Kokol, Peter
(
Mentor
)
More about this mentor...
Files:
MAG_Polanec_Mihael_2018.pdf
(2,82 MB)
MD5: FD010C520B06AF3B952B4B74A9AB7699
PID:
20.500.12556/dkum/85a759cf-4fc3-478d-88f3-4363ca2764d8
MAG_Polanec_Mihael_2018.zip
(3,20 MB)
MD5: 39D8E21F6B57D9ED8B53234F1E35DF8D
PID:
20.500.12556/dkum/0d460035-c7d7-4342-8ed4-3285ac7a67e6
Language:
Slovenian
Work type:
Master's thesis/paper
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
Place of publishing:
[Maribor
Publisher:
M. Polanec
Year of publishing:
2018
PID:
20.500.12556/DKUM-72671
UDC:
004.4\'2/.6:004.5(043.2)
COBISS.SI-ID:
21989654
NUK URN:
URN:SI:UM:DK:HO5UAMTV
Publication date in DKUM:
05.12.2018
Views:
1473
Downloads:
225
Metadata:
Categories:
KTFMB - FERI
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Licences
License:
CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:
The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:
11.10.2018
Secondary language
Language:
English
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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