Zaznavanje in napovedovanje prisotnosti napak v izvorni kodi s pomočjo metrik programske opreme in strojnega učenja

magistrsko delo

Mihael Polanec (Author), Peter Kokol (Mentor)

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;magistrske naloge;

Data

Language:	Slovenian
Year of publishing:	2018
Typology:	2.09 - Master's Thesis
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher:	M. Polanec
UDC:	004.4'2/.6:004.5(043.2)
COBISS:	21989654
Views:	740
Downloads:	150
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Fault presence detection and prediction in the source code using software metrics and machine learning
Secondary 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.
Secondary keywords:	software metrics;machine learning;software faults;
URN:	URN:SI:UM:
Type (COBISS):	Master's thesis/paper
Thesis comment:	Univ. v Mariboru, Fak. za elektrotehniko, računalništvo in informatiko, Računalništvo in informacijske tehnologije
Pages:	XIV, 96 str.
ID:	10977200