Low-code Development Research Articles

Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future Directions

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.

A search-and-fill strategy to code generation for complex software requirements

Context:The realm of software development has seen significant transformations with the rise of Low-Code Development (LCD) and the integration of Artificial Intelligence (AI), particularly large language models, into coding practices. The proliferation of open-source software also offers vast resources for developers. Objective:We aim to combine the benefits of modifying retrieved code with the use of an extensive code repository to tackle the challenges of complex control structures and multifunctional requirements in software development. Method:Our study introduces a Search-and-Fill strategy that utilizes natural language processing (NLP) to dissect complex software requirements. It extracts control structures and identifies atomic function points. By leveraging large-scale pre-trained models, the strategy searches for these elements to fill in the automatically transformed program structures derived from descriptions of control structures. This process generates a code snippet that includes program control structures and the implementations of various function points, thereby facilitating both code reuse and efficient development. Results:We have validated the effectiveness of our strategy in generating code snippets. For natural language requirements involving multifunctional complex structures, we constructed two datasets: the Basic Complex Requirements Dataset (BCRD) and the Advanced Complex Requirements Dataset (ACRD). These datasets are based on natural language descriptions and Python code that were randomly extracted and combined. For the code snippets to be generated, we achieved the best results with the ACRD dataset, with BLEU-4 scores reaching up to 0.6326 and TEDS scores peaking at 0.7807. Conclusion:The Search-and-Fill strategy successfully generates a comprehensive code snippets, integrating essential control structures and functions to streamline the development process. Experimental results substantiate our strategy’s efficacy in optimizing code reuse by effectively integrating preprocessing and selection optimization approach. Future research will focus on enhancing the recognition of complex software requirements and further refining the code snippets.

Introducing rapid web application development with Oracle APEX to students of higher education

The rapid development of information and communication technologies (ICT), and enforcement of digital transformation have caused the need to develop and deliver software in even shorter time than previously, and has become crucial to businesses. Yet, this new need has caused a challenge of finding expert IT workforce able to deliver software applications due expected time. Moreover, in a rapidly changing business world, the traditional software development can no longer keep pace and provide solutions to businesses fast enough. Low-code development and inclusion of so-called citizen developers are seen as a solution for the problem. This paper explores the introduction of rapid application development courses on Oracle APEX technology to students of a European technological university. The paper discusses the implementation of the course and its design, and looks how the developed basic-level course was received by students. It is shown that the courses have been well-received by students, who based on provided feedback are satisfied with the online set up for self-regulated learning of rapid application development skills.

Digital sourcing: A discussion of agential, semiotic, infrastructural, combinatorial, and economic shifts

For over four decades, a rich body of information systems (IS) sourcing research has examined topics concerning sourcing decisions, governing relationships and performance in various contexts (e.g., outsourcing, multisourcing and offshoring). The last few years have witnessed a significant change in some of the key assumptions in this literature. Organizations and their sourcing practices have been undergoing significant shifts in the digital era, involving not only human but also technology agency (e.g., artificial intelligence), malleable digital assets that can be flexibly recombined (e.g., in platform ecosystems and low-code development), and digital technology that attenuates bounded rationality (e.g., generative artificial intelligence). Such fundamental changes call for the re-examination of such past assumptions in the IS sourcing literature. In this editorial, we discuss the implications of these agential, semiotic, infrastructural, combinatorial, and economic shifts for digital sourcing, that is, research and practice of IS sourcing in the digital era.

Going beyond templates: composition and evolution in nested OSTRICH

AbstractLow-code frameworks strive to simplify and speed up application development. An essential mechanism to achieve these goals is to have native support for the safe reuse and usage of parameterized coarse-grain components, providing developers with strong guardrails and a rich software-building experience. —a rich template language for the OutSystems platform—was designed to simplify the use and creation of such components. Thus, the application developer can quickly reuse and assemble sophisticated and thoroughly tested application blocks. However, without a built-in composition and evolution mechanism, templates are still hard to create and maintain. This sometimes requires the repetition of code across different templates and creates a conflict between the customizations of the instantiated application models, and the update and reapplication of a template definition. This paper presents a principled mechanism for using abstraction in the creation of templates and simultaneously supporting the evolution of templates in applications after use. First, we introduce a template composition mechanism, its typing discipline, and its instantiation algorithm for model-driven low-code development environments. We start by extending to support nested templates and allow the instantiation (hatching) of templates in the definition of other templates. Nesting promotes a significant increase in code reuse potential, leading to a safer evolution of applications. We then introduce the support for customizable template instances, which allows one to evolve templates’ code and then update a template instance without losing customizations performed in the generated code. The present definition seamlessly extends the existing OutSystems metamodel with template constructs expressed by model annotations that maintain backward compatibility with the existing language toolchain. We present the metamodel, a set of annotations to support the extensions, and the corresponding validation and instantiation algorithms. In particular, we introduce a type-based validation procedure for abstractions that ensures that using templates always produces valid models. This work also extends prior developments on Nested OSTRICH with the support for safe customizations of instantiated code. We validate Nested OSTRICH using the benchmark by identifying the degree of reusability that can be reached in the existing sample of real templates and template uses. Our prototype is an extension of the OutSystems IDE that allows the annotation of models and their use to produce new models. We also analyze which existing OutSystems sample screen templates can be improved by using and sharing nested templates .

What's Wrong With Low-Code Development Platforms? An Empirical Study of Low-Code Development Platform Bugs

What's Wrong With Low-Code Development Platforms? An Empirical Study of Low-Code Development Platform Bugs

A Comprehensive Review of the Significance of Low-code Automation in Risk Management for Banks

This study provides a comprehensive examination of the influence of low-code development platforms in the risk management area within the banking sector. This research delves into the tactical utilization of low-code automation in the realm of risk management, with a particular emphasis on compliance monitoring, fraud detection, credit risk evaluation, and operational risk management. Additionally, the study centers on the metamorphosis of software design, development, and deployment processes, made possible by the easy accessibility of low-code platforms. The objective of the study is to illustrate how the digitization and automation of manual tasks in these domains augment service. provision, expedite the identification of compliance violations, and simplify credit risk evaluation, thereby conferring advantages to both banks and their clientele. Through meticulous analysis, this investigation seeks to furnish important perspectives on the uptake and influence of low-code development platforms in the banking sector, illuminating the potential for improved operational efficiency, regulatory adherence, and superior risk management.

Low-code development using requirements and knowledge representation models

Low-Code Development is a new software development paradigm which is typically used to generate web applications from high-level visual notations. These notations allow to express the User Interface and Application Logic (usersystem interactions) in a way which is understandable by the end-users. Using certain model-driven approaches, the low-code environments allow for generating the front-end layer and basic CRUD operations in the back-end. Yet still, non-standard domain logic (data processing) operations still necessitate the use of traditional programming. In this article we present a visual language, called RSL-DL that can be used to represent such non-standard domain logic in a visual form. It allows to capture domain knowledge with complex domain rules aligned with requirements models. The language synthesises and extends approaches found in knowledge representation (ontologies) and software modelling language engineering. The development environment of RSL-DL enables fully automatic generation of domain logic code by reasoning over and reusing domain knowledge. The environment includes a dedicated model editor and a transformation engine. The language?s abstract syntax is defined using a meta-model expressed in MOF. Its semantics is expressed with several translational rules that map RSL-DL models onto typical programming language constructs. The article presents a list of these rules in an informal way, and then introduces their formalisation using a graphical transformation notation. The RSL-DL environment includes an inference engine that enables processing queries to domain models and selecting appropriate invocations to generated code. It was also initially validated through studies that involve understandability, operability and complexity assessment. Based on these results, we conclude that declarative knowledge representations can be successfully used to produce imperative backend code with non-trivial logic.

ПЕРСПЕКТИВНОСТЬ И ЭФФЕКТИВНОСТЬ NO-CODE И LOW-CODE ПЛАТФОРМ ДЛЯ АВТОМАТИЗАЦИИ БИЗНЕСА

In recent years, there has been a growing interest in no-code automation tools for business processes in the IT industry. Over the last fi ve years, investments in no-code platforms have crossed one billion dollars. The study focuses on the analysis of these technologies in the context of small and large businesses. However, the issue of replacing traditional development remains. The study aims to determine who benefits from using no-code and who benefi ts from traditional evelopment. The research is relevant since companies have limited resources. Methods include effi ciency analysis and comparison of implementation results. The advantages of no-code include speed, cost, and low risks, but it is dependent on the software solution and requires programming skills. The study calls for a balance between the use of no-code and traditional development to maximize benefi ts. Based on the research, it is established that no-code and low-code development are less effective without traditional programming and will be more effective for implementation in companies that already have a team of IT specialists.

Measuring End-user Developers’ Episodic Experience of a Low-code Development Platform. A Preliminary Study

Background: As low-code development platforms (LCDPs) are becoming a trend, understanding how end-user developers think and feel as they work with such platforms is important. Particularly, assessing experiences during episodes of use can contribute to overall experience throughout long-term use. Aim: This paper aims to understand end-user developers’ episodic experience when they are building an application on a low-code platform and to provide guidance on how such experiences can be measured. Method: We designed the Episodic Developer Experience Questionnaire for LCDPs based on prior literature and refined it through expert Delphi sessions. The instrument contains 10 individual experience items, capturing various aspects of episodic experience. We further validated it through remote online tests on an LCDP. Results: The results showed significant differences in the relationships between items describing aspects of overall experience and items describing perceptions of tool quality and task difficulty. Programming expertise also affected end-user developers’ episodic experience. Conclusion: The study illustrates the design of questionnaire-based experience assessment in the context of development and identifies the importance of separating personal experience from assessment of tasks and tools since tool quality and task difficulty do not necessarily influence experience straightforwardly.

Revolutionizing Process Automation: The Synergy of Low-Code Development Platforms, Robotic Process Automation, and Integrated Smart System Platform

Revolutionizing Process Automation: The Synergy of Low-Code Development Platforms, Robotic Process Automation, and Integrated Smart System Platform

Citizen Development als Treiber der digitalen Transformation – Aktuelle Ansätze bei der Adoption von Low-Code Development Plattformen

ZusammenfassungLow-Code Development Plattformen (LCDPs) fördern die digitale Transformation von Organisationen, indem sie die Applikationsentwicklung durch FachbereichsmitarbeiterInnen ohne tiefgreifende Programmierkenntnisse – sogenannte Citizen Developer – ermöglichen. Marktforschungsinstitute prognostizieren, dass in den nächsten Jahren mehr als die Hälfte aller Applikationen mit LCDPs entwickelt werden. Nichtsdestotrotz stehen Organisationen vor der Herausforderung, sich für die richtigen Implementierungs- und Anwendungsansätze von LCDPs zu entscheiden. Dieser Artikel liefert daher ein umfassendes Bild über das praktische Verständnis und aktuelle Ansätze in verschiedenen Organisationen und leitet daraus Handlungsempfehlungen ab. Dafür wurden 16 Experteninterviews durchgeführt und wissenschaftlich analysiert. Die Ergebnisse zeigen, dass die Praxis grundsätzlich ein ähnliches Verständnis des Begriffs LCDP hat. Die Initiative für die Einführung kommt meist aus den Fachbereichen, die Entscheidung für oder gegen die LCDP-Implementierung wird jedoch meist von der Geschäftsführung in Kooperation mit der IT-Abteilung getroffen. Dabei unterscheiden sich die aktuellen Anwendungsansätze: Unternehmen nutzen entweder einen Self-Service-Ansatz durch die Fachbereiche oder integrieren die Entscheidung über eine potenzielle LCDP-Entwicklung durch die Citizen Developer in das bestehende Demand-Management der IT-Abteilung. Eine etablierte und adaptive Governance ist für beide Ansätze eine wichtige Voraussetzung. Die Erkenntnisse des Beitrags tragen zur wissenschaftlichen Diskussion bei, da dieser Artikel eine der ersten umfassenden und wissenschaftlich fundierten qualitativen Analysen über aktuelle praktische Adoptionsansätze der Praxis liefert. PraktikerInnen erfahren zudem, wie andere Unternehmen mit aktuellen Herausforderungen umgehen und welche Ansätze erfolgversprechend sind.

Using retrieval-augmented generation to elevate low-code developer skills

This article proposes applying retrieval-augmented generation (RAG) to improve the skills of low-code developers by augmenting large language models with up-to-date domain-specific knowledge. As low-code development requires combining multiple systems into a final product, developers must consult several sources of documentation and various articles, videos, and forum threads. Such a process may be time-consuming, prompting the use of an LLM for the authoritative answer. However, LLMs often lack knowledge of low-code platforms, leading to hallucinations and superficial responses. RAG utilizes the benefits of LLMs on relevant information, suggesting a presumption that it may be effectively applied in low-code development. Heterogeneous data sources concerning low-code systems are converted to a text representation, split into logical chunks, and stored in a vector database. During the exploitation of the model, cosine similarity is used to retrieve top-K documents and concatenate them with user query, using the produced text as a prompt to an LLM. The results support the hypothesis that RAG models outperform standard LLMs in knowledge retrieval in this domain

Exploring Low-Code Development: A Comprehensive Literature Review

Low-code development provides the ability to create software using visual application development tools, predefined components, and automation, minimizing the reliance on manual coding. Furthermore, it enables individuals with limited programming expertise to actively contribute to software development in collaboration with information technology (IT) professionals, improving the alignment between customer or business requirements and the developed solution. This approach speeds up the development process, fosters closer collaboration between software development and business teams, and enhances the organization’s ability to respond to business and market demands or changes. In this article, existing knowledge of low-code development from multiple perspectives is summarized, including the definitions, used tools, applied development lifecycles, application areas, potential benefits, challenges, and, consequently, relevant development and delivery principles.

Combining low-code development with ChatGPT to novel no-code approaches: A focus-group study

Low-code tools are a trend in software development for business solutions due to their agility and ease of use. There are a certain number of vendors with such solutions. Still, in most Western countries, there is a clear need for the existence of greater quantities of certified and experienced professionals to work with those tools. This means that companies with more resources can attract and maintain those professionals, whilst other smaller organizations must rely on an endless search for this scarce resource. We will present and validate a model designed to transform ChatGPT into a low-code developer, addressing the demand for a more skilled human resource solution. This innovative tool underwent rigorous validation via a focus group study, engaging a panel of highly experienced experts. Their invaluable insights and feedback on the proposed model were systematically gathered and meticulously analysed.

Efficient Model-Driven Prototyping for Edge Analytics

Software development cycles in the context of IoT! (IoT!) applications require the orchestration of different technological layers, and involve complex technical challenges. The engineering team needs to become experts in these technologies and time delays are inherent due to the cross-integration process because they face steep learning curves in several technologies, which leads to cost issues, and often to a resulting product that is prone to bugs. We propose a more straightforward approach to the construction of high-quality IoT applications by adopting model-driven technologies (DIME and Pyrus), that may be used jointly or in isolation. The presented use case connects various technologies: the application interacts through the EdgeX middleware platform with several sensors and data analytics pipelines. This web-based control application collects, processes and displays key information about the state of the edge data capture and computing that enables quick strategic decision-making. In the presented case study of a Stable Storage Facility (SSF), we use DIME to design the application for IoT connectivity and the edge aspects, MongoDB for storage and Pyrus to implement no-code data analytics in Python. We have integrated nine independent technologies in two distinct Low-code development environments with the production of seven processes and pipelines, and the definition of 25 SIBs in nine distinct DSLs. The presented case study is benchmarked with the platform to showcase the role of code generation and the reusability of components across applications. We demonstrate that the approach embraces a high level of reusability and facilitates domain engineers to create IoT applications in a low-code fashion.

Dandelion: A scalable, cloud-based graphical language workbench for industrial low-code development

Dandelion: A scalable, cloud-based graphical language workbench for industrial low-code development

Benefits and limitations of using low-code development to support digitalization in the construction industry

Low-code development is a technology enabling people with no advanced computing or coding skills to develop custom-made applications and digital solutions to address specific operational needs. Low-code has gained significant momentum in the last years and some studies have already reported the use of this technology to digitalize workflows and processes in a variety of business scenarios. Nevertheless, to-date there is no research evaluating the implications of this technology to support digitalization in construction industry. Based on a hybrid research approach combining literature review, case study, and surveys, this paper addresses this gap by evaluating the benefits and limitations of using low-code in the construction industry context. The study reveals that low-code productive application development practices have the potential to enable different degrees of digitalization and innovation in specific construction processes. The study also recognizes the relevance of adopting a strategic approach to fully leverage low-code at an organizational construction firm level and identifies several areas for further research. This includes leveraging synergies with industry-relevant subjects such as lean construction, Building Information Modelling, the Internet of Things, and Industry 4.0, among others.

MakeTwin: A reference architecture for digital twin software platform

Digital twin is currently undergoing a significant transformation from the conceptual and theoretical research phase to the implementation and application phase. However, a universally adaptable research and development platform for digital twin is critically needed to meet the development requirements. Specifically, a publicly accessible simulation, testing, and validation platform which can support digital twin model building, data processing, algorithm design, configuration, etc., is urgently required for researchers. Furthermore, for developers from the industry, a low-code development platform that can offer a customizable suite of functions such as model creation, data management, protocol configuration, and visualization is urgently needed. Meanwhile, for enterprise users, there is a lack of an application management platform that can be configured and migrated for various application scenarios, functions, and modes. Therefore, based on the system research of digital twin theories and key technologies by the authors (such as the five-dimension digital twin model, digital twin modeling and digital twin data theory, digital twin standards, and so on), a digital twin software platform reference architecture, namely makeTwin, is proposed and designed, as well as its ten core functions. The workflow of the makeTwin and the interaction mechanisms among its core functions are described. Finally, a digital twin application system for a chemical fiber textile shop floor (CFTS) which was developed according to makeTwin, is introduced, which validates the proposed reference architecture.

How AI can move customer personalisation to customer contextualisation

Reaching beyond personalisation to achieve contextualisation is the holy grail for brands and retailers — a challenge that countless businesses hope to crack by applying emerging artificial intelligence (AI) technologies. International business leaders are increasing their investments in technology and data analytics to drive performance. This includes building out marketing teams with data scientists and acquiring tech start-ups to transform their businesses’ capabilities to more accurately predict customer needs and wants. McDonald’s US$300m acquisition of AI start-up Dynamic Yield is a clear example, described as fuelling the business’s vision to create more personalised customer experiences. In the race to tailor communications and customise recommendations, however, few businesses can deliver contextualisation — the ability to understand what a customer wants, immediately, at a particular moment in time. Advances in voice interfaces and consumer use of personal assistants only accentuate the need for greater sophistication in analysing situational context. This paper shares a view of the emerging AI-fuelled voice and virtual assistant ecosystem, how businesses can drive demonstrable value by solving the contextualisation challenge, and how new low-code development practices can accelerate and democratise these coming technological advances.