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Title:Primerjava samodejne in manualne ocene količine tehničnega dolga : magistrsko delo
Authors:ID Hliš, Tilen (Author)
ID Pavlič, Luka (Mentor) More about this mentor... New window
ID Stropnik, Ambrož (Comentor)
Files:.pdf MAG_Hlis_Tilen_2020.pdf (2,60 MB)
MD5: 35B8C0444E9139553333A5DA9C502E84
PID: 20.500.12556/dkum/d9b7fb7a-dd29-444f-a03f-2fc1223c8e53
 
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 raziskali področje metod za ocenjevanje količine tehničnega dolga. Izvedli smo sistematičen pregled literature, s katerim smo raziskali metode, ki se uporabljajo v orodjih za samodejno ocenjevanje količine tehničnega dolga. Analizirali in zbrali smo orodja, ki te metode implementirajo. Na petih izbranih projektih razvoja informacijskih rešitev smo izvedli popis tehničnega dolga. Pri tem smo najprej izvedli manualno identifikacijo in ocenjevanje količine tehničnega dolga. Sledila je samodejna identifikacija in ocenjevanje količine tehničnega dolga s pomočjo orodij. V zaključnem delu popisovanja smo dobljene manualne in samodejne ocene med seboj primerjali in analizirali. Empirični podatki, pridobljeni v popisu nakazujejo na velik razkorak med manualno in samodejno oceno.
Keywords:tehničen dolg, metode ocenjevanja, orodja, merjenje
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[T. Hliš]
Year of publishing:2020
Number of pages:XI, 80 str.
PID:20.500.12556/DKUM-76606 New window
UDC:659.2:004(043.2)
COBISS.SI-ID:27330819 New window
NUK URN:URN:SI:UM:DK:0IZ5UWHS
Publication date in DKUM:03.07.2020
Views:1681
Downloads:260
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:14.06.2020

Secondary language

Language:English
Title:Comparing the automatic and the manual estimates of the technical debt amount
Abstract:In this master thesis, we report a researched in the field of the methods of the technical debt amount estimation. We conducted a systematic literature review to investigate the methods used in a tools for automatically estimating the amount of technical debt. We analysed and collected the tools that implement these methods. We collected technical debt items on a five selected software projects, in which we firstly performed manual identification and estimation of the amount of technical debt. We followed by automatic identification and assessment. In the final part, the obtained manual and automatic estimates were compared and analysed. The empirical data obtained indicate a gap between manual and automatic estimation.
Keywords:technical debt, methods for estimating, tools, measurement


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