Low-code Development Research Articles

Analysis of Low Code-No Code Development Platforms in comparison with Traditional Development Methodologies

Abstract: This paper examines the overview of low-code/no-code development platforms in comparison with traditional development methodologies and examines the benefits and limitations of the same. For several decades, businesses have had multiple options when they demanded new information systems. They could develop a new system using in-house developers, or they could order a system from an external merchant. This offers a close fit to business obligations. However, nowadays, there is a new alternative that is becoming increasingly prevalent. Low code/no code (LC/NC) applications can cater to business requirements efficiently, can be implemented instantly, and the cost is much less than systems developed in-house. Few, if any, programming skills are required. Keywords: Traditional development, No code development, low code development, Low code No code development, Software development life cycle (SDLC)

Factors that affect the utilization of low-code development platforms: survey study

Factors that affect the utilization of low-code development platforms: survey study

Data Spine: A Federated Interoperability Enabler for Heterogeneous IoT Platform Ecosystems.

Today, the Internet of Things (IoT) is pervasive and characterized by the rapid growth of IoT platforms across different application domains, enabling a variety of business models and revenue streams. This opens new opportunities for companies to extend their collaborative networks and develop innovative cross-platform and cross-domain applications. However, the heterogeneity of today’s platforms is a major roadblock for mass creation of IoT platform ecosystems, pointing at the current absence of technology enablers for an easy and innovative composition of tools/services from the existing platforms. In this paper, we present the Data Spine, a federated platform enabler that bridges IoT interoperability gaps and enables the creation of an ecosystem of heterogeneous IoT platforms in the manufacturing domain. The Data Spine allows the ecosystem to be extensible to meet the need for incorporating new tools/services and platforms. We present a reference implementation of the Data Spine and a quantitative evaluation to demonstrate adequate performance of the system. The evaluation suggests that the Data Spine provides a multitude of advantages (single sign-on, provision of a low-code development environment to support interoperability and an easy and intuitive creation of cross-platform applications, etc.) over the traditional approach of users joining multiple platforms separately.

Managing non-trivial internet-of-things systems with conversational assistants: A prototype and a feasibility experiment

Internet-of-Things has reshaped the way people interact with their surroundings and automatize the once manual actions. In a smart home, controlling the Internet-connected lights is as simple as speaking to a nearby conversational assistant. However, specifying interaction rules, such as making the lamp turn on at specific times or when someone enters the space is not a straightforward task. The complexity of doing such increases as the number and variety of devices increases, along with the number of household members. Thus, managing such systems becomes a problem, including finding out why something has happened. This issue lead to the birth of several low-code development solutions that allow users to define rules to their systems, at the cost of discarding the easiness and accessibility of voice interaction. In this paper we extend the previous published work on Jarvis [1], a conversational interface to manage IoT systems that attempts to address these issues by allowing users to specify time-based rules, use contextual awareness for more natural interactions, provide event management and support causality queries. A proof-of-concept is presented, detailing its architecture and natural language processing capabilities. A feasibility experiment was carried with mostly non-technical participants, providing evidence that Jarvis is intuitive enough to be used by common end-users, with participants showcasing an overall preference by conversational assistants over visual low-code solutions.

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

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). 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. 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. 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. By applying the proposed DCM we were able to predict three types of interaction problems felt by first-time users of the LCDP. 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.

Low-Code as Enabler of Digital Transformation in Manufacturing Industry

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.