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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.

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 transition towards digital pathology and an extensive selection of video conferencing platforms have helped provide continuity to education even during the COVID-19 pandemic. Innovative approaches for pathology education, will likely persist beyond the pandemic, as they have powerful didactic potential. While there is a wide selection of software for use as educational tools, an environment to access all resources with ease is clearly lacking. In this technical note, we highlight our customized educational applications built using a low-code approach. Our applications, developed with Microsoft Power Apps, serve both educational and examination purposes and are launched using Microsoft Teams. Building applications using a low-code approach has made our applications very specific to our use and enabled daily distanced education. Combined with existing features on Teams, such as file sharing, meeting scheduling, and messaging, the applications serve as a unique and customizable pathology educational platform.

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.

PurposeThe purpose of this study is to understand the concept of “Low Code No Code” applications and study its scope of application for web designing, rapid application development (RAD) and supply chain digitization (SCD).Design/methodology/approachA qualitative exploratory study was conducted for this exploratory study. A semi-structured open-ended questionnaire was prepared by the authors. Based on the questionnaire in-depth interviews were conducted with subject matter experts having more than 10 years of experience in the domain of supply chain management and digitization. The study questionnaire focused on the current reach and future potential of “Low Code No Code” platforms. A total of 20 responses were collected from experts as post this point thematic saturation was reached. A non-probabilistic convenience sampling was applied to identify the experts The data was content analyzed for themes.FindingsThe major findings that emerged from the study was that “Low Code No Code” platforms applications could be used across end-to-end SCD. The study also revealed that RAD through “Low Code No Code” platforms could reduce organizations dependency on coders. In the case of procurement, “Low Code No Code” applications could improve vendor and supplier management by streamlining processes. The cost-effective and easy-to-maintain “Low Code No Code” application development could help Medium and Small-Scale Enterprises level the playing field against large organizations. The lack of adoption strategy and low perceived usefulness was identified as major barriers to the adoption of “Low Code No Code” applications by organizations.Research limitations/implications“Low Code No Code” application-based automation would enable better utilization of organizational supply chain (SC) resources and capabilities. This would improve the sustainability performance of the firm. Furthermore, it would also enable the provision of SC services at a lower cost level, thus benefiting customers.Practical implications“Low Code No Code” application-based automation would help organizations to reduce the dependency on coders and Information Technology developers SCD. This could also allow SC managers to make more apps to be built in less time without the need of complex coding. This could potentially reduce app development costs toward digitizing SCs.Originality/valueTo the best of the authors’ knowledge, this was one of the very first studies regarding how “Low Code No Code” applications could revolutionize the SC using these app development capabilities. This study also provided an extensive study of Diffusion of Innovations and Technological Organizational Theory frameworks for in the context of “Low Code No Code” technology adoption.

The rapid development of next-generation sequencing platforms has enabled the use of sequencing for routine genotyping across a range of genetics studies and breeding applications. Genotyping-by-sequencing (GBS), a low-cost, reduced representation sequencing method, is becoming a common approach for whole-genome marker profiling in many species. With quickly developing sequencing technologies, adapting current GBS methodologies to new platforms will leverage these advancements for future studies. To test new semiconductor sequencing platforms for GBS, we genotyped a barley recombinant inbred line (RIL) population. Based on a previous GBS approach, we designed bar code and adapter sets for the Ion Torrent platforms. Four sets of 24-plex libraries were constructed consisting of 94 RILs and the two parents and sequenced on two Ion platforms. In parallel, a 96-plex library of the same RILs was sequenced on the Illumina HiSeq 2000. We applied two different computational pipelines to analyze sequencing data; the reference-independent TASSEL pipeline and a reference-based pipeline using SAMtools. Sequence contigs positioned on the integrated physical and genetic map were used for read mapping and variant calling. We found high agreement in genotype calls between the different platforms and high concordance between genetic and reference-based marker order. There was, however, paucity in the number of SNP that were jointly discovered by the different pipelines indicating a strong effect of alignment and filtering parameters on SNP discovery. We show the utility of the current barley genome assembly as a framework for developing very low-cost genetic maps, facilitating high resolution genetic mapping and negating the need for developing de novo genetic maps for future studies in barley. Through demonstration of GBS on semiconductor sequencing platforms, we conclude that the GBS approach is amenable to a range of platforms and can easily be modified as new sequencing technologies, analysis tools and genomic resources develop.

The growing use of machine learning (ML) and artificial intelligence across sectors has shown strong potential to improve decision-making processes. However, the adoption of ML by non-technical professionals remains limited due to the complexity of traditional development workflows, which often require software engineering and data science expertise. In recent years, low-code and no-code platforms have emerged as promising solutions to democratize ML by abstracting many of the technical tasks typically involved in software engineering pipelines. This paper investigates whether these platforms can offer a viable alternative for making ML accessible to non-expert users. Beyond predictive performance, this study also evaluates usability, setup complexity, the transparency of automated workflows, and cost management under realistic “out-of-the-box” conditions. This multidimensional perspective provides insights into the practical viability of LC/NC tools in real-world contexts. The comparative evaluation was conducted using three leading cloud-based tools: Amazon SageMaker Canvas, Google Cloud Vertex AI, and Azure Machine Learning Studio. These tools employ ensemble-based learning algorithms such as Gradient Boosted Trees, XGBoost, and Random Forests. Unlike traditional ML workflows that require extensive software engineering knowledge and manual optimization, these platforms enable domain experts to build predictive models through visual interfaces. The findings show that all platforms achieved high accuracy, with consistent identification of key features. Google Cloud Vertex AI was the most user-friendly, SageMaker Canvas offered a highly visual interface with some setup complexity, and Azure Machine Learning delivered the best model performance with a steeper learning curve. Cost transparency also varied considerably, with Google Cloud and Azure providing clearer safeguards against unexpected charges compared to Sagemaker Canvas.

Low-code platforms aim at allowing non-experts to develop complex systems and knowledgeable developers to improve their productivity in orders of magnitude. The greater gain comes from using components developed by experts capturing common patterns across all layers of the application, from the user interface to the data layer and integration with external systems. Often, cloning sample code fragments is the only alternative in such scenarios, requiring extensive adaptation to reach the intended use. Such customization activities require deep knowledge outside of the comfort zone of low code. To effectively speed up the reuse, composition, and adaptation of pre-defined components, low-code platforms need to provide safe and easy-to-use language mechanisms. This paper introduces OSTRICH, a strongly typed rich templating language for a low-code platform (OutSystems) that builds on metamodel annotations and allows the correct instantiation of templates. We conservatively extend the existing metamodel and ensure that the resulting code is always well-formed. The results we present include a novel type safety verification of template definitions, and template arguments, providing model consistency across application layers. We implemented this template language in a prototype of the OutSystems platform and ported nine of the top ten most used sample code fragments, thus improving the reuse of professionally designed components.

In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparameters, it is worth trying out the automatic machine learning features provided by several cloud-based and cloud-agnostic platforms. This paper explores the possibility of generating automatic machine learning models with low-code experience. We developed criteria to compare different machine learning platforms for generating automatic machine learning models and presenting their results. Thereafter, lessons learned by developing automatic machine learning models from a sample dataset across four different machine learning platforms were elucidated. We also interviewed machine learning experts to conceptualize their domain-specific problems that automatic machine learning platforms can address. Results showed that automatic machine learning platforms can provide a fast track for organizations seeking the digitalization of their businesses. Automatic machine learning platforms help produce results, especially for time-constrained projects where resources are lacking. The contribution of this paper is in the form of a lab experiment in which we demonstrate how low-code platforms can provide a viable option to many business cases and, henceforth, provide a lane that is faster than the usual hiring and training of already scarce data scientists and to analytics projects that suffer from overruns.

Automated code generation and process flow management are central to web-based application development today. This database-centric approach targets the form and process management challenges faced by corporate companies. It minimizes the time losses caused by managing hundreds of forms and processes, especially in large companies. Shortening development times, optimizing user interaction, and simplifying the code are critical advantages offered by this methodology. These low-code systems accelerate development, allowing organizations to adapt to the market quickly. This approach simplifies the development process with drag-and-drop features and enables developers to produce more effective solutions with less code. Automatic code generation with flow diagrams allows one to manage inter-page interactions and processes more intuitively. The interactive Process Design Editor developed in this study makes code generation more user-friendly and accessible. The case study results show that a 98.68% improvement in development processes, a 95.84% improvement in test conditions, and a 36.01% improvement in code size were achieved with this system. In conclusion, automated code generation and process flow management represent a significant evolution in web application development processes. This methodology both shortens development times and improves code quality. In the future, the demand for these technologies is expected to increase even more.