A model-driven approach for the development of native mobile applications focusing on the data layer

Núñez, Manuel; Bonhaure, Daniel; González, Magalí; Cernuzzi, Luca

doi:10.1016/j.jss.2019.110489

Cited by 20 publications

(21 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MoWebA Mobile [39] is a model-driven approach covering the mobile apps' data layer. This approach mainly defines the data source of application to develop offline access to business applications in case of network connectivity issues.…”

Section: Dsl For Energy Specificationmentioning

confidence: 99%

See 1 more Smart Citation

eGEN: An Energy-saving Modeling Language and Code Generator for Location-sensing of Mobile Apps

Boyalakuntla¹,

C²,

Chimalakonda³

et al. 2022

Preprint

View full text Add to dashboard Cite

The demand for reducing the energy consumption of location-based applications has increased in recent years. The abnormal battery-draining behavior of GPS makes it difficult for the developers to decide on battery optimization during the development phase directly. It will reduce the burden on developers if battery-saving strategies are considered early, and relevant battery-aware code is generated from the design phase artifacts. Therefore, we aim to develop tool support, eGEN, to specify and create native location-based mobile apps. eGEN consists of Domain-specific Modeling Language (DSML) and a code generator for location-sensing. It is developed using Xtext and Xtend as an Eclipse plug-in, and currently, it supports native Android apps. eGEN is evaluated through controlled experiments by instrumenting the generated code in five location-based open-source Android applications. The experimental results show 4.35 minutes of average GPS reduction per hour and 188 mA of average reduction in battery consumption while showing only 97 meters degrade in location accuracy over 3 kilometers of a cycling path. Hence, we believe that code generated by eGEN would help developers to balance between energy and accuracy requirements of location-based applications. The source code, documentation, tool demo video 1 , and tool installation video 2 are available at https://github.com/Kowndinya2000/egen.

show abstract

Section: Dsl For Energy Specificationmentioning

confidence: 99%

“…This approach consists of three phases: [21] , Xmob [30], ADSML [26] uses the textual DSL to specify the app functionalities. The graphical DSL is used in the approaches like DSL-Comet [55], RAPPT [2], and MoWebA Mobile [39].…”

Section: Dsl For Energy Specificationmentioning

confidence: 99%

eGEN: An Energy-saving Modeling Language and Code Generator for Location-sensing of Mobile Apps

Boyalakuntla¹,

C²,

Chimalakonda³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, the developers need to compile the generated source code using the corresponding development environment such as the Android Developer Tools or Xcode. In [12], the authors propose a model-driven approach for the development of native mobile applications with a focus on the data layer. The development process starts by defining meta-models and Architecture-Specific Model (ASM) profiles.…”

Section: B Model-based Approaches For Mobile Applicationsmentioning

confidence: 99%

An Automated Model-Based Approach for Developing Mobile User Interfaces

Ammar

2021

IEEE Access

View full text Add to dashboard Cite

The ever-increasing number of mobile platforms constitutes a challenge for application developers, who must develop efficient mobile applications for multiple platforms. Recently, a specific interest is being taken in the Model Based User Interface Development (MBUID) by Software Engineering Community. In such paradigm, an application's user interface is obtained by defining models and transformations of those models. This paper aim at adopting MBUID paradigm to address the problem of mobile application development. As such, we propose a new approach and its support system for the automatic generation of mobile user interfaces. The approach and the system are based on a set of standards and relevant technologies such as EMF, GMF, ATL, and Xpand. A case study is presented, in the paper, with the aim of verifying the usefulness of this approach.

show abstract

“…In the contemporary industry environment, cloud computing can play a premier role in the transformation process since it is a type of outsourcing technology that combines many scalable, normally personalised computing servers and inexpensive infrastructure (Wang et al 2016). The data access tier provides entry to data located within the practical solution boundaries or data available through remote cloud service providers (Núñez et al 2020).…”

Section: Architecture and Characteristics Of Developed Solution For Nonconformity Managementmentioning

confidence: 99%

A novel JavaScript MEAN stack approach for real-time nonconformity management in SMEs within Industry 4.0 and Quality 4.0 concepts

Đorđević¹,

Stefanović²,

Cvetić³

et al. 2021

Preprint

View full text Add to dashboard Cite

Nonconformity detection and reporting are evergoing issues in quality management practice and theory. Throughout industrialisation, and consequently, in the Industry 4.0 period, manufacturing organisations strived to reduce nonconformities to the minimum level and reach zero-defect production. Nowadays, it is feasible to overcome this issue by incorporating adequate information and communication technologies and edge devices for nonconformity reporting, and nonconformity classification. These assumptions were a starting point for the research presented in this paper. This paper aims to propose a software solution for nonconformity detection and preventive and corrective actions definition that expands the utilisation of edge devices in compliance with Industry 4.0 and Quality 4.0 paradigms. Presented software solution design is based on JavaScript programming language and its ability to be implemented in all software solution's tiers through interconnected frameworks (MongoDB, Express.js, Angular, and Node.js) in a MEAN stack. The developed solution is implemented in three small and medium enterprises. Initial results show several benefits in increasing nonconformity detection and reporting, increasing employee participation in preventive and corrective actions definition and improved quality management system. The number of corrective and preventive actions was taken based on the assistance of the prediction module. All nonconformities were related to specific sections of ISO 9001:2015 standards so that quality managers and managers in the companies could have insight into the sources of the issues and the foundation for defining different managerial actions. When comparing the newly developed solution and other affordable solutions, it was determined that the new solution has a higher operating velocity if there is a significant increase in the volume of queries towards databases. The paper's main contribution reflects in the software solution's presentation intended for affordable identification and nonconformities massive workload reporting, integrated with other software modules for quality analysis, prediction, and problem-solving. In this way, it is possible to obtain horizontal scalability and richness of the proposed software solution for smart enterprises focused on World Class manufacturing and lean manufacturing. Furthermore, it is possible to achieve a shorter time for nonconformities elimination and better compliance with the ISO 9001: 2015 standard. The presented solution usage contributes to all participants involved in the organisation of production and production itself, through (1) involvement of all employees; (2) digitalisation and improvement of existing nonconformity reporting systems; (3) improvement of nonconformity perception; (4) improved awareness and evaluation of employer's contribution to nonconformity management; (5) making the technology easy to use. The research's novelty lies in presented solution utilisation for real-time nonconformity classification and proactive and corrective measures proposals through communication and prediction modules.

show abstract

A model-driven approach for the development of native mobile applications focusing on the data layer

Cited by 20 publications

References 12 publications

eGEN: An Energy-saving Modeling Language and Code Generator for Location-sensing of Mobile Apps

eGEN: An Energy-saving Modeling Language and Code Generator for Location-sensing of Mobile Apps

An Automated Model-Based Approach for Developing Mobile User Interfaces

A novel JavaScript MEAN stack approach for real-time nonconformity management in SMEs within Industry 4.0 and Quality 4.0 concepts

Contact Info

Product

Resources

About