iOS Applications Testing

Kulesovs, Ivans

doi:10.17770/etr2015vol3.187

Cited by 7 publications

(1 citation statement)

References 16 publications

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“…W kolejnym artykule [6] poddano analizie antywzorce wydajności dotyczące aplikacji na system iOS, bez wyróżnienia zastosowanych szkieletów programistycznych. W artykule [7] dokonano przeglądu literatury, dotyczącego testowania aplikacji iOS, gdzie wyróżniono różne aspekty wpływające na wydajność testowanych urządzeń takie jak animacje, obsługa gestów czy zarządzanie danymi. W obecnym czasie nie znaleziono artykułów naukowych poruszających tematykę porównawczą przedmiotowych szkieletów programistycznych, natomiast pomimo krótkiego czasu istnienia SwiftUI napisanych zostało wiele książek na jego temat.…”

Section: Wstępunclassified

Comparative analysis of UIKit and SwiftUI frameworks in iOS system

Wiertel

Skublewska‐Paszkowska

2021

jcsi

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The paper concerns a comparison of SwiftUI and UIKit frameworks, used in iOS application development. SwiftUI was introduced in 2019 as a successor to UIKit as a main tool for creating application views. The purpose of this article is to compare the time performance of these two frameworks. Six testing applications have been implemented for the research, 2 in each framework. The software was complementary. The defined thesis "SwiftUI is more time efficient for applications with data collection and many filled text fields" was proved.

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Section: Wstępunclassified

Comparative analysis of UIKit and SwiftUI frameworks in iOS system

Wiertel

Skublewska‐Paszkowska

2021

jcsi

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Benefitting from the Grey Literature in Software Engineering Research

Garousi

Felderer

Mäntylä

et al. 2020

Contemporary Empirical Methods in Software Engineering

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Researchers generally place the most trust in peer-reviewed, published information, such as journals and conference papers. By contrast, software engineering (SE) practitioners typically do not have the time, access or expertise to review and benefit from such publications. As a result, practitioners are more likely to turn to other sources of information that they trust, e.g., trade magazines, online blog-posts, survey results or technical reports, collectively referred to as Grey Literature (GL). Furthermore, practitioners also share their ideas and experiences as GL, which can serve as a valuable data source for research. While GL itself is not a new topic in SE, using, benefitting and synthesizing knowledge from the GL in SE is a contemporary topic in empirical SE research and we are seeing that researchers are increasingly benefitting from the knowledge available within GL. The goal of this chapter is to provide an overview to GL in SE, together with insights on how SE researchers can effectively use and benefit from the knowledge and evidence available in the vast amount of GL.

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