lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Multi-version software quality analysis through mining software repositories
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
2018 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The main objective of this thesis is to identify how the software repository features influence software quality during software evolution. To do that the mining software repository area was used. This field analyzes the rich data from software repositories to extract interesting and actionable information about software systems, projects and software engineering. The ability to measure code quality and analyze the impact of software repository features on software quality allows us to better understand project history, project quality state, development processes and conduct future project analysis. Existing work in the area of software quality describes software quality analysis without a connection to the software repository features. Thus they lose important information that can be used for preventing bugs, decision-making and optimizing development processes. To conduct the analysis specific tool was developed, which cover quality measurement and repository features extraction. During the research general procedure of the software quality analysis was defined, described and applied in practice. It was found that there is no most influential repository feature and the correlation between software quality and software repository features exist, but it is too small to make a real influence.

Place, publisher, year, edition, pages
2018. , p. 50
Keywords [en]
software quality analysis, mining software repositories, software repository features, quality measurement, quality metrics
National Category
Computer Sciences Software Engineering
Identifiers
URN: urn:nbn:se:lnu:diva-74424OAI: oai:DiVA.org:lnu-74424DiVA, id: diva2:1210221
Subject / course
Computer Science
Educational program
Software Technology Programme, Master Programme, 60 credits
Presentation
2018-02-28, B3033V, Hus B, Videum, Vejdes plats 7, Växjö, 09:00 (English)
Supervisors
Examiners
Available from: 2018-06-04 Created: 2018-05-27 Last updated: 2018-06-04Bibliographically approved

Open Access in DiVA

17HT-4DV50E-Thesis-Project-Report-Illia-Kriukov(2475 kB)4 downloads
File information
File name FULLTEXT01.pdfFile size 2475 kBChecksum SHA-512
f4a1442bcb5d354a856da2c8e2642f0bcb150837c98f01f4f0e6e37b76e0aaf3e5a785dc1c63137cd4251caf4127c98ccca61ab7451b9a3515b1993356eaf705
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Kriukov, Illia
By organisation
Department of computer science and media technology (CM)
Computer SciencesSoftware Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 4 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 15 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf