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Source code quality in connection to self-admitted technical debt
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
2020 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The importance of software code quality is increasing rapidly. With more code being written every day, its maintenance and support are becoming harder and more expensive. New automatic code review tools are developed to reach quality goals. One of these tools is SonarQube. However, people keep their leading role in the development process. Sometimes they sacrifice quality in order to speed up the development. This is called Technical Debt. In some particular cases, this process can be admitted by the developer. This is called Self-Admitted Technical Debt (SATD). Code quality can also be measured by such static code analysis tools as SonarQube. On this occasion, different issues can be detected. The purpose of this study is to find a connection between code quality issues, found by SonarQube and those marked as SATD. The research questions include:

1) Is there a connection between the size of the project and the SATD percentage?

2) Which types of issues are the most widespread in the code, marked by SATD?

3) Did the introduction of SATD influence the bug fixing time?

As a result of research, a certain percentage of SATD was found. It is between 0%–20.83%. No connection between the size of the project and the percentage of SATD was found. There are certain issues that seem to relate to the SATD, such as “Duplicated code”, “Unused method parameters should be removed”, “Cognitive Complexity of methods should not be too high”, etc. The introduction of SATD has a minor positive effect on bug fixing time.

We hope that our findings can help to improve the code quality evaluation approaches and development policies

Place, publisher, year, edition, pages
2020. , p. 65
Keywords [en]
Self-admitted technical debt, technical debt, bug, issue, SonarQube, code quality
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:lnu:diva-97977OAI: oai:DiVA.org:lnu-97977DiVA, id: diva2:1464776
Subject / course
Computer Science
Educational program
Software Technology Programme, Master Programme, 60 credits
Presentation
2020-06-04, online, 10:00 (English)
Supervisors
Examiners
Available from: 2020-09-15 Created: 2020-09-07 Last updated: 2020-09-15Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • 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