Low Code Development Platform for Digital Transformation

2021 ◽  
pp. 689-697
Author(s):  
Vaishali S. Phalake ◽  
Shashank D. Joshi
Keyword(s):  
Digital Transformation ◽  
Development Platform ◽  
Code Development

scholarly journals Using a Low Code Development Environment to Teach the Agile Methodology

2021 ◽  
pp. 191-199
Author(s):  
Mary Lebens ◽  
Roger Finnegan
Keyword(s):  
Higher Education ◽  
Digital Transformation ◽  
Comfort Level ◽  
Agile Development ◽  
Development Environment ◽  
Business World ◽  
Development Platform ◽  
Agile Methodology ◽  
Development Methodology ◽  
Code Development

AbstractThe Agile development methodology is soaring in popularity in the business world. Companies are turning to Agile to develop products quickly and to achieve digital transformation of their organization. Because of this push, companies need employees who understand Agile. Therefore, higher education is obligated to provide an understanding of Agile to students as they enter the workplace. Providing Agile experience to students who are new to programming is difficult because they are so worried about the coding aspects of the assignment, they cannot take time to think about the methodology they are using. The coding crowds out the time needed to get an understanding of how Agile actually works. One remedy for this is to use a low or no-code development platform. With this type of platform students spend less time learning to create apps, freeing them to experience the rituals and roles of Agile. This study examines using the Agile methodology along with the Microsoft Power Apps platform to provide an Agile experience to students. Two course sections were surveyed to learn if students perceived that they acquired a better understanding of Agile and to learn their perceptions of a no-code platform experience. The students completed surveys to ascertain their comfort with the Agile methodology and whether the no-code environment increased their comfort level. The results showed students perceived the no-code platform increased their comfort with using the Agile methodology. The implication is that no-code platforms can be used broadly to help students to gain experience with Agile.

scholarly journals Development and Validation of a Descriptive Cognitive Model for Predicting Usability Issues in a Low-Code Development Platform

2020 ◽  
pp. 001872082092042
Author(s):  
Carlos Silva ◽  
Joana Vieira ◽  
José C. Campos ◽  
Rui Couto ◽  
António N. Ribeiro
Keyword(s):  
Cognitive Model ◽  
Decomposition Methods ◽  
End Users ◽  
Successful Completion ◽  
Development Platform ◽  
Knowledge Based ◽  
Development And Validation ◽  
Criticality Analysis ◽  
First Time ◽  
Code Development

Objective The aim of the study was the development and evaluation of a Descriptive Cognitive Model (DCM) for the identification of three types of usability issues in a low-code development platform (LCDP). Background LCDPs raise the level of abstraction of software development by freeing end-users from implementation details. An effective LCDP requires an understanding of how its users conceptualize programming. It is necessary to identify the gap between the LCDP end-users’ conceptualization of programming and the actions required by the platform. It is also relevant to evaluate how the conceptualization of the programming tasks varies according to the end-users’ skills. Method DCMs are widely used in the description and analysis of the interaction between users and systems. We propose a DCM which we called PRECOG that combines task decomposition methods with knowledge-based descriptions and criticality analysis. This DCM was validated using empirical techniques to provide the best insight regarding the users’ interaction performance. Twenty programmers (10 experts, 10 novices) were observed using an LCDP and their interactions were analyzed according to our DCM. Results The DCM correctly identified several problems felt by first-time platform users. The patterns of issues observed were qualitatively different between groups. Experts mainly faced interaction-related problems, while novices faced problems attributable to a lack of programming skills. Conclusion By applying the proposed DCM we were able to predict three types of interaction problems felt by first-time users of the LCDP. Application The method is applicable when it is relevant to identify possible interaction problems, resulting from the users’ background knowledge being insufficient to guarantee a successful completion of the task at hand.

scholarly journals Low-Code as Enabler of Digital Transformation in Manufacturing Industry

2019 ◽  
Vol 10 (1) ◽  
pp. 12 ◽  
Author(s):  
Raquel Sanchis ◽  
Óscar García-Perales ◽  
Francisco Fraile ◽  
Raul Poler
Keyword(s):  
Manufacturing Industry ◽  
Digital Transformation ◽  
Current Status ◽  
Context Analysis ◽  
Virtual Factory ◽  
Software Applications ◽  
Other Information ◽  
Code Domain ◽  
Iot Devices ◽  
Code Development

Currently, enterprises have to make quick and resilient responses to changing market requirements. In light of this, low-code development platforms provide the technology mechanisms to facilitate and automate the development of software applications to support current enterprise needs and promote digital transformation. Based on a theory-building research methodology through the literature and other information sources review, the main contribution of this paper is the current characterisation of the emerging low-code domain following the foundations of the computer-aided software engineering field. A context analysis, focused on the current status of research related to the low-code development platforms, is performed. Moreover, benchmarking among the existing low-code development platforms addressed to manufacturing industry is analysed to identify the current lacking features. As an illustrative example of the emerging low-code paradigm and respond to the identified uncovered features, the virtual factory open operating system (vf-OS) platform is described as an open multi-sided low-code framework able to manage the overall network of a collaborative manufacturing and logistics environment that enables humans, applications, and Internet of Things (IoT) devices to seamlessly communicate and interoperate in the interconnected environment, promoting resilient digital transformation.

HIMSS Impact18: Leading Digital Transformation und Big Data in Medicine

2018 ◽  
Vol 23 (09) ◽  
pp. 25-25
Author(s):  
Sabine Schützmann
Keyword(s):  
Big Data ◽  
Digital Transformation

Am 17. und 18. Oktober findet im Hasso-Plattner-Institut (HPI) in Potsdam zum zweiten Mal die HIMSS Impact statt: Ein englischsprachiges Symposium, welches aktuelle Trends im Gesundheitswesen, digitale Strategien und jüngste Forschungserkenntnisse beleuchtet.

An old friend is better than two new ones? Approaches to market research in the context of digital transformation for the antitrust laws enforcement

2020 ◽  
pp. 37-55 ◽  
Author(s):  
A. E. Shastitko ◽  
O. A. Markova
Keyword(s):  
Business Models ◽  
Rapid Development ◽  
Digital Transformation ◽  
Market Definition ◽  
Type I ◽  
Type Ii ◽  
Digital Platforms ◽  
Advantages And Disadvantages ◽  
Pass Through ◽  
Type Ii Errors

Digital transformation has led to changes in business models of traditional players in the existing markets. What is more, new entrants and new markets appeared, in particular platforms and multisided markets. The emergence and rapid development of platforms are caused primarily by the existence of so called indirect network externalities. Regarding to this, a question arises of whether the existing instruments of competition law enforcement and market analysis are still relevant when analyzing markets with digital platforms? This paper aims at discussing advantages and disadvantages of using various tools to define markets with platforms. In particular, we define the features of the SSNIP test when being applyed to markets with platforms. Furthermore, we analyze adjustment in tests for platform market definition in terms of possible type I and type II errors. All in all, it turns out that to reduce the likelihood of type I and type II errors while applying market definition technique to markets with platforms one should consider the type of platform analyzed: transaction platforms without pass-through and non-transaction matching platforms should be tackled as players in a multisided market, whereas non-transaction platforms should be analyzed as players in several interrelated markets. However, if the platform is allowed to adjust prices, there emerges additional challenge that the regulator and companies may manipulate the results of SSNIP test by applying different models of competition.

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