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Title:Primerjalna analiza orodij umetne inteligence za generiranje programske kode: učinkovitost, varnost in vpliv na razvoj aplikacij : učinkovitost, varnost in vpliv na razvoj aplikacij
Authors:ID Novak, David (Author)
ID Šumak, Boštjan (Mentor) More about this mentor... New window
Files:.pdf VS_Novak_David_2026.pdf (1,30 MB)
MD5: 5A5F498A68FA580BEA5078E448F5301A
 
.zip VS_Novak_David_2026.zip (23,49 KB)
MD5: ED5078416F76B132EA2C53264A8393F0
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Diplomsko delo obravnava uporabo generativne umetne inteligence pri generiranju programske kode. Namen raziskave je bil primerjati orodja ChatGPT, GitHub Copilot, Microsoft Copilot, DeepSeek in Grok pri reševanju manjših implementacijskih nalog v programskem jeziku Python. Orodja so bila primerjana glede na natančnost, robustnost, berljivost in čas generiranja začetne programske rešitve. Raziskava je vključevala eksperimentalno testiranje petih programskih nalog in anketni ter intervjujski del. Rezultati kažejo, da se orodja razlikujejo predvsem pri obravnavi napak, strukturiranosti kode in hitrosti generiranja. Orodji DeepSeek in GitHub Copilot sta ustvarili bolj robustne in strukturirane rešitve, orodje Grok pa je izstopalo po krajšem času generiranja. Rezultati ne predstavljajo neposredne mere celotne produktivnosti razvijalca, saj celoten čas dokončanja naloge ni bil merjen.
Keywords:umetna inteligenca, generiranje kode, produktivnost razvijalcev, varnost kode
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[D. Novak]
Year of publishing:2026
Number of pages:1 spletni vir (1 datoteka PDF (IX, 75 str.))
PID:20.500.12556/DKUM-98583 New window
UDC:004.8(043.2)
COBISS.SI-ID:289390083 New window
Publication date in DKUM:18.08.2026
Views:171
Downloads:26
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:29.06.2026

Secondary language

Language:English
Title:Comparative analysis of artificial intelligence tools for code generation: effectiveness, security, and impact on application development : diplomsko delo
Abstract:The thesis examines the use of generative artificial intelligence in software code generation. The aim of the research was to compare the tools ChatGPT, GitHub Copilot, Microsoft Copilot, DeepSeek, and Grok in solving smaller implementation tasks in the Python programming language. The tools were compared in terms of accuracy, robustness, readability, and the time required to generate the initial software solution. The research included experimental testing of five programming tasks, as well as a survey and interviews. The results show that the tools differ mainly in error handling, code structure, and generation speed. DeepSeek and GitHub Copilot generated more robust and structured solutions, while Grok stood out with a shorter generation time. The results do not represent a direct measure of overall developer productivity, as the total time required to complete the task was not measured.
Keywords:artificial intelligence, code generation, developer productivity, code security


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