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Algorithms have evolved from machine code to low-code-no-code (LCNC) in the past 20 years. Observing the growth of LCNC-based algorithm development, the CEO of GitHub mentioned that the future of coding is no coding at all. This paper systematically reviewed several of the recent studies using mainstream LCNC platforms to understand the area of research, the LCNC platforms used within these studies, and the features of LCNC used for solving individual research questions. We identified 23 research works using LCNC platforms, such as SetXRM, the vf-OS platform, Aure-BPM, CRISP-DM, and Microsoft Power Platform (MPP). About 61% of these existing studies resorted to MPP as their primary choice. The critical research problems solved by these research works were within the area of global news analysis, social media analysis, landslides, tornadoes, COVID-19, digitization of process, manufacturing, logistics, and software/app development. The main reasons identified for solving research problems with LCNC algorithms were as follows: (1) obtaining research data from multiple sources in complete automation; (2) generating artificial intelligence-driven insights without having to manually code them. In the course of describing this review, this paper also demonstrates a practical approach to implement a cyber-attack monitoring algorithm with the most popular LCNC platform.

Low-code development platforms (LCDPs) such as Mendix, OutSystems, and Microsoft Power Platform are reshaping software development. These platforms enable non-technical users to develop applications by reusing and configuring out-of-the-box components in visual, user-friendly environments. While LCDPs are typically portrayed as new standalone development tools, they also exhibit many characteristics of digital platform ecosystems. For example, similar to traditional app stores, they also offer marketplaces where external developers and business users can contribute what they have developed. Yet, despite these conceptual similarities, the “platform” dimension of LCDPs has received little attention. Accordingly, this Fundamentals paper conceptualizes LCDPs as a distinct type of digital platform ecosystem. We build on research in digital platform ecosystems, boundary resources, packaged software reuse, and platform governance, and explain how LCDPs align with and differ from conventional platforms. Specifically, we show how LCDPs combine infrastructure and reuse, embed design constraints directly into components, and orchestrate ecosystem participation through a logic of “curated enablement” rather than open contribution. We conclude by outlining a research agenda that highlights LCDPs not as marginal tools for end-user development, but as rich, theory-relevant settings for further exploration.

The article presents a method for evaluating Low-Code Development Platforms (LCDPs) for the purpose of assessing the degree to which the expectations of users of these platforms are met in terms of the possibility of independent software development by non-IT-trained specialists—the “Citizen Development” (CD). The degree of meeting the CD assumptions has been implemented in the proprietary CD Score index. It enables comparison of existing LCDPs and formulation of recommendations regarding platform selection, taking into account project team skills. The results of research aimed at systematizing available LCDPs represent an attempt to define their preliminary taxonomy. The attached examples of evaluation of existing LCDPs illustrate the possibilities of using this new measure in practice.

The Low Code Development Platform (LCDP) is a versatile platform to handle process, database, mobile and web based applications. The platform provides us opportunity to digitize the activities in IT, telecommunication, government and all other industries as well as different departments of the organization in the form of applications. The manufacturing industry can also incorporate low-code apps for data analysis and their manufacturing processes to automate the process. This research work has proposed a novel sustainable LCDP with optimization techniques for data analysis. The proposed platform allows user to execute data analysis applications in optimization and without optimization mode. The result shows that optimized LCDP reduces both space and time required for the any type of application.

Low-Code Development Platforms (LCDPs) empower users to create and deploy custom software with little to no programming. These platforms streamline development, offering benefits like faster time-to-market, reduced technical barriers, and broader participation in software creation, even for those without traditional coding skills. This study explores the application of LCDPs in Precision Agriculture (PA) through a systematic literature review (SLR). By analyzing the general characteristics and challenges of LCDPs, alongside insights from existing PA research, we assess their feasibility and potential impact in agricultural contexts. Our findings suggest that LCDPs can enable farmers and agricultural professionals to create tailored applications for real-time monitoring, data analysis, and automation, enhancing farming efficiency. However, challenges such as scalability, extensibility, data security, and integration with complex IoT systems must be addressed to fully realize the benefits of LCDPs in PA. This study contributes to the growing knowledge base in agricultural technology, offering valuable insights for researchers, practitioners, and policymakers looking to leverage LCDPs for sustainable and efficient farming practices.

In this paper, we introduce the idea of the ‘visibility matrix’ for automated data entry form generation in low-code development platforms. We then focus on the problem of software development productivity in the area of automated software generation as the main factor of the Industry 4.0 concept in the area of business information. In our study, two different approaches to user interface development in a business process management low-code platform were evaluated. The first, the multi-form model, assumes that input forms are prepared separately for each user task in the business process being automated. The second approach, the single-form model, assumes that there is one global input form for every task in the business process. Since users have access to different data in different process tasks, it is necessary to prepare the visibility matrix to define which data are relevant to which tasks. The experiments presented in this paper help to answer the following question: which approach yields better results in terms of productivity, which is measured as costs and time required to prepare the application? Several dozen real business processes were analysed to examine the properties of their visibility matrix. Additionally, the real project team members were evaluated to determine their productivity. Then, the productivity parameters were calculated for real business processes and real project teams. The results show which approach is better suited for real-world business process development.

The last few years have witnessed a significant growth of so-called low-code development platforms (LCDPs) both in gaining traction on the market and attracting interest from academia. LCDPs are advertised as visual development platforms, typically running on the cloud, reducing the need for manual coding and also targeting non-professional programmers. Since LCDPs share many of the goals and features of model-driven engineering approaches, it is a common point of debate whether low-code is just a new buzzword for model-driven technologies, or whether the two terms refer to genuinely distinct approaches. To contribute to this discussion, in this expert-voice paper, we compare and contrast low-code and model-driven approaches, identifying their differences and commonalities, analysing their strong and weak points, and proposing directions for cross-pollination.

The rise of citizen developers utilizing Low Code/No Code (LC/NC) platforms marks a transformative shift in the software development landscape. As organizations face a shortage of skilled software developers, this study addresses the urgent need to leverage citizen developers – individuals without traditional technical backgrounds who can effectively contribute to software solutions using LC/NC platforms. Through a multi-layered research methodology that includes a literature review, analysis of job postings, and qualitative interviews, this paper provides a detailed characterization of the citizen developer role and delineates the specific skills and competencies necessary for success. The findings reveal that citizen developers typically occupy supplementary roles within organizations, highlighting the need for versatile, cross-functional skill sets that meet the evolving demands of modern businesses.To address this landscape, the study proposes a comprehensive framework designed to facilitate the integration of citizen developers into organizational IT strategies. This framework underscores the importance of aligning of re- and upskilling initiatives with organizational goals while fostering a culture of continuous learning. By doing so, this paper contributes to the discourse on evolving IT roles and socio-technical systems development. It offers a strategic roadmap for harnessing the potential of citizen developers to mitigate the developer shortage and enhance organizational agility in software development.

The creation of blockchain-based software applications requires today considerable technical knowledge, particularly in software design and programming. This is regarded as a major barrier in adopting this technology in business and making it accessible to a wider audience. As a solution, low-code and no-code approaches have been proposed that require only little or no programming knowledge for creating full-fledged software applications. In this paper we extend a review of academic approaches from the discipline of model-driven engineering as well as industrial low-code and no-code development platforms for blockchains. This includes a content-based, computational analysis of relevant academic papers and the derivation of major topics. In addition, the topics were manually evaluated and refined. Based on these analyses we discuss the spectrum of approaches in this field and derive opportunities for further research.

Citizen developers and low-code platforms considerably change how software is created because the development of business applications transgresses the boundaries of organizational IT departments. With these changes, a new generation of developers must be trained. Such training is considered the responsibility of higher education institutions that enthusiastically adopt practice-based and experiential learning approaches. At the core, the focus is on real-world experiences and practical problem-solving, albeit with growing concerns about limiting deep learning and metacognitive reflections. Ample educational research highlights that metacognition is essential for students to be workplace-ready, but very little research has investigated the impact of practice-based learning on metacognitive reflections. This study draws on experiential learning theory to examine the influence of experiences in a low-code systems development project on metacognitive thinking. In a quantitative study using a survey, the effects of experiential learning factors (i.e., authenticity, active learning, self-relevance, and utility) and a team-based learning factor on metacognitive reflections are tested. Results show that when citizen developers create business applications, the experiential learning factors positively impact metacognitive reflections. However, team-based learning only positively moderates the relationship between self-relevance and reflections, whereas it negatively moderates the relationship between authenticity and reflections. Taken as a whole, the study's contributions suggest that practice-based learning using low-code platforms under the citizen developer method is an effective way to train IT talents with needed context and life-long self-learning skills.