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Title:Analiza vektorizirane izvorne kode s strojnim učenjem
Authors:Miloševič, Aleksej (Author)
Karakatič, Sašo (Mentor) More about this mentor... New window
Files:.pdf MAG_Milosevic_Aleksej_2020.pdf (3,93 MB)
MD5: A6D2DA75FBD1F4F594845243E3C1C999
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Statična analiza izvorne kode je pomemben del razvoja programske opreme, ki pa ima nekaj pomembnih pomanjkljivosti, ker z metrik programske kode ne moremo sklepati o semantični pravilnosti. Kot potencialno rešitev smo v magistrskem delu raziskali nevronsko mrežo Code2Vec. V teoretičnem delu smo obravnavali osnovne koncepte umetnih nevronskih mrež, tehnike redukcij dimenzionalnosti vektorjev in delovanje Code2Vec. V praktičnem delu smo izvedli eksperiment vizualizacije, klasifikacije in gručenja nad podatkovno množico, ki jo sestavljajo tako visoko-dimenzionalni vektorji kot tudi splošne značilnosti programske kode metod šestih odprtokodnih projektov. Glede na rezultate sklepamo, da so vektorji Code2Vec koristni za izvedbo statične analize kode.
Keywords:umetne nevronske mreže, vektorizacija, Code2Vec, izvorna koda, strojno učenje
Year of publishing:2020
Publisher:[A. Miloševič]
Source:Maribor
UDC:004.8:004.415.3(043.2)
COBISS_ID:23085334 New window
NUK URN:URN:SI:UM:DK:AQKX6WSZ
Views:533
Downloads:154
Metadata:XML RDF-CHPDL DC-XML DC-RDF
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:10.12.2019

Secondary language

Language:English
Title:Analysis of vectorized source code with machine learning
Abstract:Static software analysis as an integral part of software development has important flaws because it is impossible to discern the semantic structure and correctness of software from metrics alone. In this work, we have researched the neural network Code2Vec as a potential solution to this problem. In the theoretical segment, we have described the basics of neural networks, dimensionality reduction techniques and the inner workings of Code2Vec. In the practical segment, we have conducted a visualization, classification and clustering experiment using a dataset comprised of standard features as well as Code2Vec code embeddings of Java methods. We can conclude from the results of the experiment that Code2Vec embeddings are an appropriate tool for static code analysis.
Keywords:artificial neural networks, code embedding, Code2Vec, source code, machine learning


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