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dc.contributor.author
Charisiadis, Christos
en
dc.date.accessioned
2018-05-19T10:36:10Z
dc.date.available
2018-05-20T00:00:13Z
dc.date.issued
2018-05-19
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29154
dc.rights
Default License
dc.title
Data Mining on Source Code
en
heal.type
masterThesis
en_US
heal.keywordURI.LCSH
Software engineering
heal.keywordURI.LCSH
Data mining
heal.keywordURI.LCSH
Computer software
heal.keywordURI.LCSH
Computer software--Quality control
heal.keywordURI.LCSH
Computer software--Evaluation
heal.keywordURI.LCSH
Reliability (Engineering)
heal.keywordURI.LCSH
Computer software--Validation--Methodology
heal.keywordURI.LCSH
Computer software -- Reliability
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Information & Communication Technology Systems
en_US
heal.publicationDate
2018-05-19
heal.abstract
This dissertation was written as a part of the MSc in ICT Systems at the International
Hellenic University.
Software companies and software engineers have always tried to find ways to improve
the quality of their projects. However, assessing the quality of a piece of software is not
something trivial. There are
many
parameters that can affect that, and most of t
he time it
is very hard to find objective ways to measure the quality of software.
In this dissertation, I will provide a methodology that aims to assess software using automatically extracted software metrics and community based metrics, which will be Git
Hub
Stars and Forks. This way we can use both static metrics and dynamic ones
,
in order to
try and predict the reusability of a new piece of software.
en
heal.advisorName
Tjortjis, Christos
el
heal.committeeMemberName
Berberidis, Christos
en
heal.committeeMemberName
Gatzianas, Marios
en
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US
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