Source code classification using neural networks

Kanavakis, Eleftherios

dc.contributor.author	Kanavakis, Eleftherios	en
dc.date.accessioned	2020-11-27T10:43:23Z
dc.date.available	2020-11-27T10:43:23Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/52075
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.19773
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Source code classification	en
dc.subject	AST	en
dc.subject	Compilers	en
dc.subject	LSTM	en
dc.subject	HAN	en
dc.subject	Ταξινόμηση πηγαίου κώδικα	el
dc.subject	Μεταγλωττιστές	el
dc.subject	Αφηρημένο συντακτικό δέντρο (AST)	el
dc.subject	Αναδρομικά νευρωνικά δίκτυα	el
dc.subject	Τεχνικές προ-επεξεργασίας πηγαίου κώδικα	el
dc.title	Source code classification using neural networks	el
dc.contributor.department	CSlab	en
heal.type	bachelorThesis
heal.classification	Computer Science	en
heal.language	el
heal.language	en
heal.access	campus
heal.recordProvider	ntua	el
heal.publicationDate	2020-09-09
heal.abstract	The purpose of this dissertation is to study the problem of source code classification using neural networks. More specifically, in this problem, a piece of code is classified into an algorithmic class based on the function it performs. Pre-existing research has shown that neural networks are an effective way of modeling source code and solving such classification problems. Although literature results are encouraging, there are limitations related not only to datasets and preprocessing techniques but also to machine learning models. To this end, we propose a system that initially builds quality datasets, which are free of biases and noise. It then uses compilers to process these sets and finally uses neural networks to classify them into an algorithmic class. In the context of optimizing the system above, we studied a variety of preprocessing techniques and machine learning models.	en
heal.advisorName	Goumas, Georgios	en
heal.committeeMemberName	Georgios, Goumas	en
heal.committeeMemberName	Koziris, Nektarios	en
heal.committeeMemberName	Pneumatikatos, Dionisios	en
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	123 p.	en
heal.fullTextAvailability	false