SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Show document

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)
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
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
Source:Maribor
License:CC BY-NC-ND 4.0
This work is available under this license: Creative Commons Attribution Non-Commercial No Derivatives 4.0 International
Views:37
Downloads:8
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
:
  
Average score:(0 votes)
Your score:Voting is allowed only for logged in users.
Share:AddThis
AddThis uses cookies that require your consent. Edit consent...

Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

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


Comments

Leave comment

You have to log in to leave a comment.

Comments (0)
0 - 0 / 0
 
There are no comments!

Back
Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica