Sökning: "Technology Debt"
Visar resultat 16 - 20 av 145 uppsatser innehållade orden Technology Debt.
16. Trends in the Capital Structure and Risk Assessment of Swedish Real Estate Companies : A Study on the Impact of the 2022-2023 Shift in Interest Rates
Master-uppsats, KTH/Fastighetsföretagande och finansiella systemSammanfattning : This study aims to analyse the changes in the capital structure of Swedish real estate companies over the past five years, with a particular focus on the period 2022-2023, characterised by the policy interest rate increasing from zero to 3.5 percent. LÄS MER
17. A campaign concept with the purpose of encouraging a target audience to seek information about credit and personal debt
Kandidat-uppsats, Linköpings universitet/Medie- och Informationsteknik; Linköpings universitet/Tekniska fakultetenSammanfattning : .... LÄS MER
18. Arkitektonisk teknisk skuld : Hantering, utmaningar och risktaganden bland molnen
Master-uppsats, Uppsala universitet/Informationssystem; Uppsala universitet/Företagsekonomiska institutionenSammanfattning : Arkitektoniska beslut har ofta långvariga effekter på system. När beslut leder till ökad komplexitet, underhållbarhet och hämmad utvecklingshastighet, uppstår arkitektonisk teknisk skuld. LÄS MER
19. Flytt i samband med renovering
Master-uppsats, Lunds universitet/FastighetsvetenskapSammanfattning : The growing need for renovations is one of the key challenges within the Swedish multifamily rental stock. Sustainable development is essential and renovations affect all three major categories of sustainability, the social, economic and ecological factors. All buildings need to be remodeled sooner or later – the question is only when and how. LÄS MER
20. Code smells in machine learning pipelines: an MSR sample study
Kandidat-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknikSammanfattning : As technical debt in software engineering projects continues to negatively impact the development process, this study focuses on technical debt in form of code smells in machine learning pipelines and in code written by data scientists. This study contributes to the body of knowledge on technical debt as it tries to quantify the assumption in the literature that scientists without a software engineering background struggle with software engineering’s best practices when writing code. LÄS MER