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Title:Vpliv pomanjkljive programske kode na vrednost tehničnega dolga
Authors:ID Rednjak, Zlatko (Author)
ID Heričko, Marjan (Mentor) More about this mentor... New window
Files:.pdf MAG_Rednjak_Zlatko_2017.pdf (1,85 MB)
MD5: 5B13870B0BAE8F7DDB0F287B577B60D7
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu smo predstavili področje tehničnega dolga in pomanjkljive kode ter raziskali povezavo med tipi pomanjkljive kode zaznanimi z izbranimi orodji in privzetimi pravili v orodju SonarQube. Za raziskavo omenjene povezave smo klasificirali 12 pravil orodja SonarQube v različne tipe pomanjkljive kode. Na podlagi kriterijev smo izbrali 32 projektov, orodje JSpIRIT in orodje JDeodorant ter tri najbolj pogosto analizirane tipe pomanjkljive kode. Empirični podatki dobljeni z analizo izbranih projektov so statistično analizirani in nakazujejo na težave pri preslikavi pravil orodja SonarQube v tipe pomanjkljive kode. Posledično je skoraj nemogoče definirati povezavo med zaznanimi pomanjkljivimi kodami v izbranih orodjih in pravili v orodju SonarQube.
Keywords:programske rešitve, zaznavanje pomanjkljive kode, tehnični dolg, Java, SonarQube, JSpIRIT, JDeodorant
Place of publishing:Maribor
Publisher:[Z. Rednjak]
Year of publishing:2017
PID:20.500.12556/DKUM-65508 New window
UDC:004.4'2:004.65(043.2)
COBISS.SI-ID:20560406 New window
NUK URN:URN:SI:UM:DK:WUKHZ5MW
Publication date in DKUM:24.04.2017
Views:1618
Downloads:218
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:The impact of code smells on technical debt value
Abstract:In this master thesis, we present the area of code smells and technical debt. We focus our research on the correlation between code smells detected by chosen tools and the default rules defined in SonarQube. To help establish the correlation we classified 12 rules defined in SonarQube into different types of code smells. Based on the defined criteria we selected 32 projects, tools JSpIRIT and JDeodorant and three most analyzed code smell types. Empirical data obtained through analysis of chosen projects is statistically analyzed and it indicates on problems when mapping SonarQube’s rules into different types of code smells. As a result it is almost impossible to define a correlation between code smells detected by chosen tools and rules defined in SonarQube.
Keywords:software, code smell identification, technical debt, Java, SonarQube, JSpIRIT, JDeodorant


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