magistrsko delo
Tilen Hliš (Author), Luka Pavlič (Mentor), Ambrož Stropnik (Co-mentor)

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;magistrske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher: [T. Hliš]
UDC: 659.2:004(043.2)
COBISS: 27330819 Link will open in a new window
Views: 1041
Downloads: 152
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Other data

Secondary language: English
Secondary title: Comparing the automatic and the manual estimates of the technical debt amount
Secondary 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.
Secondary keywords: technical debt;methods for estimating;tools;measurement;
Type (COBISS): Master's thesis/paper
Thesis comment: Univ. v Mariboru, Fak. za elektrotehniko, računalništvo in informatiko, Informatika in tehnologije komuniciranja
Pages: XI, 80 str.
ID: 11831573