Achieving Business Practicability of Model-Driven Cross-Platform Apps

Due to the incompatibility of mobile device platforms such as Android and iOS, apps have to be developed separately for each target platform. Cross-platform development approaches based on Web technology have significantly improved over the last years. However, since they do not lead to native apps, these frameworks are not feasible for all kinds of business apps. Moreover, the way apps are developed is cumbersome. Advanced cross-platform approaches such as MD2, which is based on model-driven development (MDSD) techniques, are a much more powerful yet less mature choice. We discuss business implications of MDSD for apps and introduce MD2 as our proposed solution to fulfill typical requirements. Moreover, we highlight a business-oriented enhancement that further increases MD2's business practicability. We generalize our findings and sketch the path towards more versatile MDSD of apps

Analysis of Feature-Completeness in Android Cross-Platform Frameworks

In cross-platform mobile development research, we frequently encounter mentions of limitations and constraints potentially imposed by technical tools and development frameworks.This is especially prominent in the context of programmatic device- and platform feature access, including features such as GPS, Internet and device camera access.Although the majority of the literature does not empirically validate these claims, they have reached acceptance in both practitioners' communities and academic research.By downloading a sample of 300,000 Android applications available on the Google Play Store and analysing them, we set forth to find which platform- and device features are the most commonly included in deployed apps.Based on the results, we map the features to their availability in five major cross-platform development frameworks, thus provide an overview of feature completeness and potential shortcomings in these popular frameworks.Our findings indicate that the scrutinised frameworks range from 86.37% to 95.46% feature-completeness and can thus facilitate the development of mobile apps relying on features that are commonly found in our assessed sample of Android apps

Codeless App Development: Evaluating A Cloud-Native Domain-Specific Functions Approach

Mobile applications play an important role in the economy today and there is an increasing trend for app enablement on multiple platforms. However, creating, distributing, and maintaining an application remain expert tasks. Even for software developers, the process can be error-prone and resource-consuming, especially when targeting different platforms simultaneously. Researchers have proposed several frameworks to facilitate cross-platform app development, but little attention has been paid to non-technical users. In this paper, we described the Flow framework, which takes the advantage of domain-specific languages to enable no-code specification for app modeling. The cloud-native coordination mechanism further supports non-technical users to execute, monitor, and maintain apps for any target platforms. User evaluations were conducted to assess the usability and user experience with the system. The results indicated that users can develop apps in Flow with ease, but the prototype could be optimized to reduce learning time and workload

Baseline Requirements for Comparative Research on Cross-Platform Mobile Development: A Literature Survey

Technical implementations are common in computing research to objectively assess hypotheses. In mobile computing, and more specifically within research on cross-platform mobile development, such implementations are usually in the form of mobile apps. Due to the lack of a common ground for research on app development, studies tend to lack depth and miss out on possible contributions. In an attempt to better the situation, we propose a technical baseline for future research on cross-platform app development to draw from based on previous studies’ technical implementations. We assess and scrutinize existing literature to find trends, and use the generated knowledge to lay out the baseline proposa