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Legacy systems are valuable assets in most public sector agencies that have been in use for a long time. These systems support government service delivery to the citizens and maintain vital public administration functions and data. However, legacy systems are often related to technical difficulties that impede innovation efforts. The maintenance of the systems has become challenging and incompatible with the demands of digital transformation in the public sector. Due to their importance, the systems cannot be easily discarded. Rebuilding the old systems from scratch entails a long development timeline, high cost, and the loss of critical service functionalities. These circumstances encourage the public sector agencies to implement the modernisation of legacy systems. However, the modernisation effort for legacy systems in the public sector is not straightforward. Besides technical aspects, it should also consider non-technical aspects, including the requirements of the new era of citizen-centric digital government. In order to achieve this aspiration, a complete strategy must be developed to serve as a guide for government agencies. Hence, the purpose of this study is to develop a comprehensive guideline for the public sector. The research has been developed using a qualitative methodology that incorporates the theoretical and empirical phases. The theoretical phase was conducted through a literature review of previous studies related to the research topic. The empirical phase in the public sector was implemented and analysed using phenomenology and grounded theory methods. A total of 19 informants were involved in the individual and focus group interviews conducted. The study results revealed that human, process, product, and organisation aspects as well as the related characteristics of the citizen-centric influence the legacy systems modernisation in the era of digital government. The findings contribute as a complete guideline for the public sector agencies in modernising the legacy systems in line with the citizen-centric digital government vision.

Information technology and communication (ICT) plays an important role as a catalyst for organisational development and innovation. However, old information systems that are known as legacy systems often expose organisations to the risk of business failure. These systems are not only impeding the advancement in technology strategy but also hindering the organisations’ business competitiveness. Nevertheless, legacy systems are essential in supporting critical functions in organisations including the public sector and could not be scrapped easily. These systems need to be given a new strength through modernisation to continue providing the best service in line with global trends. Modernisation is a complex task that involves several related aspects. In the context of the public sector, legacy systems involve a complicated information relationship, environment, and culture, while ensuring the citizens are of high priority. The implementation of a digital government represents the transformation of the public service delivery to the citizens that emphasises a citizen-centric design. This study, therefore, aims to address this concern by reviewing the factors involved and suggesting a guideline in the form of a conceptual model to assist in the modernisation of legacy systems for a citizen-centric digital government. Data from the theoretical study were analysed using content analysis. The results show that the legacy systems’ modernisation comprised four main aspects, namely human, process, product, and organisation aspects, with related factors and elements. This model contributes as a reference for the public sector and provides overall guidance in performing legacy systems modernisation.

Microservice architecture (MSA) has garnered attention in various software communities because of its significant advantages. Organizations have also prioritized migrating their legacy systems to MSA, seeking to gather the intrinsic advantages of this architectural style. Despite the importance of this architectural style, there is a lack of comprehensive studies in the literature on the modernization of legacy systems to MSA. Thus, the principal objective of this article is to present a comprehensive overview of this research theme through a mixed-method investigation composed of a systematic mapping study based on 43 studies and an empirical evaluation by industry practitioners. From these, a taxonomy for the initiatives identified in the literature is established, along with the application domain for which such initiatives were designed, the methods used to evaluate these initiatives, the main quality attributes identified in our investigation, and the main activities employed in the design of such initiatives. As a result, this article delineates a process of modernization based on six macro-activities, designed to facilitate the transition from legacy systems to microservice-based ones. Finally, this article presents a discussion of the results based on the evidence gathered during our investigation, which may serve as a source of inspiration for the design of new initiatives to support software modernization.

Organisations often find themselves trapped in a costly cycle of replacing legacy systems. Jidoka is a software development methodology that aims to break this cycle and for organisations to mature into a mode of continuous modernisation. Jidoka, which loosely translates to automation with a human touch, offers several strategies for developers to modernise legacy systems onto their preferred technology stack, ensuring that quality, security, and other compliance is met. The paper presents state-of-practice information on how the process emerged through the use of Model-Driven Engineering (MDE), DevOps, and lessons learned using other software development methodologies across 100+ projects.

The replacement of legacy systems in the public sector is fraught with project delays, budgetary overruns, technological and business process complexities. Moreover, the software implemented to replace legacy systems is developed or configured to largely mimic their features and functionality in order to minimize the disruption to organizational operations that accompanies the introduction of new technology. When the requirements for legacy replacement primarily replicate existing applications and processes, opportunities for business process improvement are bypassed. However, it is difficult for practitioners to transcend the business and systems models prevalent in their organizations for many years. The challenge is to support them to overcome such a difficulty, and be creative and engaged during requirements discussions. Our research aims to evaluate the utility of gamifying the requirements activities during legacy replacement projects for scoping replacement systems efforts in a way that takes advantage of opportunities for innovation while minimizing unnecessary changes to the status quo. The supplementation of the requirements process with game elements is explored in our research through the development of a requirements discussion game (RE-PROVO) and its evaluation by practitioners in two government agencies. Our findings reveal that key elements of RE-PROVO, including competition, anonymity and roleplay, encourage a more critical evaluation of business requirements in legacy replacement projects, but that the success of requirements gamification is contingent on a proper incentivization model which takes organizational culture and values into account.

Human-Robot Interaction (HRI) has emerged as a critical component in contemporary manufacturing systems, particularly within the domain of collaborative robotics, where intuitive interfaces play a vital role in improving productivity, efficiency, and safety. This study seeks to meet the major challenges of human-robot cooperation (e.g. scalability, economical, integration with legacy systems, task flexibility, and usability). The proposed solution will address the development of a modular and scalable solution to rapidly scale cobots in broad manufacturing environments, from small to large global supply chains. The research ensures small and medium enterprises (SMEs) can use collaborative robotics without prohibitive costs; by presenting cost effective solutions such as cloud-based interfaces and low-cost sensor integration. Furthermore, the framework enhances collaboration by enabling robots to adapt dynamically, switch seamlessly between tasks, and learn from real-time feedback. The challenge therefore is still to overcome the integration bottleneck, ensuring that collaborative robots can be easily integrated into existing legacy systems, to enable minimal downtime and implementation costs. Finally, the intuitive, easy-to-use user interface requires minimal training, allowing workers to work safely alongside robots to maximize productivity and provide a safer workplace overall. By enhancing human-robot collaboration in modern manufacturing, this research contributes to developing efficient, flexible, and sustainable industrial practices by offering a new capability that is both scalable and cost-effective.

As smart manufacturing environments continue to evolve, operational technology systems are increasingly integrated with external networks and cloud-based platforms. However, many manufacturing facilities still use legacy systems running on end-of-support/life operating systems with discontinued security updates. It is difficult to mitigate the cyber threats and risks for these systems using perimeter-based security models that isolate them from other networks. To address these constraints, a Zero Trust-based security architecture tailored for legacy manufacturing environments with practical field applicability is proposed. Our architecture builds upon the six core functions outlined in National Institute of Standards and Technology Cybersecurity Framework 2.0—identify, protect, detect, respond, recover, and govern—adapting them specifically to manufacturing environment security challenges. To achieve this, the architecture combines asset identification, policy-driven access control, secure SMB gateway transfers, automated anomaly detection and response, clean image recovery, and organizational governance procedures. This study validates the effectiveness and scalability of the proposed architecture through scenario-based simulations. When combining the EoSL defense hardening and gateway-based perimeter control, the architecture achieves approximately 99% overall threat suppression and a 98% reduction in critical-asset infection rates, demonstrating its strong resilience and scalability in large-scale legacy OT environments.

Integrating third-party and legacy systems has become a critical necessity for companies, driven by the need to exchange information with various entities such as banks, suppliers, customers, and partners. Ensuring data integrity, keeping integrations up-to-date, reducing transaction risks, and preventing data loss are all vital aspects of this complex task. Achieving success in this endeavor, which involves both technological and business challenges, necessitates the implementation of a well-suited architecture. This article introduces an architecture known as the Dynamic Canonical Data Model through Agnostic Messages. The proposal addresses the integration of loosely coupled software units, mainly when dealing with internal and external data integration. To illustrate the architecture’s components, a case study from the Mexican Logistics Company Paquetexpress is presented. This organization manages integrations across several platforms, including SalesForce and Oracle ERP, with clients like Amazon, Mercado Libre, Grainger, and Afull. Each of these incurs costs ranging from USD 30,000 to USD 36,000, with consultants from firms such as Quanam, K&F, TSOL, and TekSi playing a crucial role in their execution. This consumes much time, making maintenance costs considerably high when clients request data transmission or type changes, particularly when utilizing tools like Oracle Integration Cloud (OIC) or Oracle Service Bus (OSB). The article provides insights into the architecture’s design and implementation in a real-world scenario within the delivery company. The proposed architecture significantly reduces integration and maintenance times and costs while maximizing scalability and encouraging the reuse of components. The source code for this implementation has been registered in the National Registry of Copyrights in Mexico.

IDMS (Integrated Database Management System) has long been a backbone for mission-critical systems in finance, healthcare, and government sectors. However, the rigid architecture of legacy systems poses challenges in scalability, flexibility, and integration with modern technologies. This paper explores IDMS modernization using Artificial Intelligence (AI), with a focus on predictive maintenance, query optimization, and cloud integration. Through real-world implementations, the integration of AI-driven solutions has shown transformative potential: query response times were reduced by 25%, unscheduled downtime decreased by 30%, and system scalability improved by accommodating a 40% increase in traffic without degradation. By leveraging AI-powered automation and modern cloud infrastructures, IDMS can achieve database optimization and real-time operational efficiency. This work highlights how AI ensures the relevance and competitiveness of IDMS, enabling it to meet the demands of modern legacy systems and ensuring its sustained role in critical business operations.

Retrofitting legacy systems provides significant advantages by addressing compatibility issues with new devices and technologies, meeting current process requirements, and increasing security and regulatory compliance. The process starts by collecting requirements and evaluating the legacy system’s attributes and limitations, followed by integrating modern technologies to improve efficiency, streamline processes, and enhancing performance and interoperability while leveraging existing facilities to reduce costs. A systematic approach ensures that updates align with modern technological standards, with performance evaluations conducted via qualitative and quantitative methods and system maturity assessed according to the Reference Architecture Model for Industries 4.0 (RAMI 4.0 model’s) criteria for intelligent factories. By incorporating digital twin (DT) capabilities, which replicate the physical state of systems and provide real-time data updates, the retrofit strategy aligns the physical system with Industry 4.0 contexts, facilitating continuous improvement and seamless integration with modern processes. The goal is to advance the legacy system technologically to ensure seamless integration with contemporary processes, validated through RAMI criteria analysis for smart factories. As part of this process, digital twin architecture was built. This architecture was the basis for building and operating digital twins in the process. The methodology was used to enhance and transform legacy systems, creating the foundation for creating a fully digital twin. Using this method, these systems can be updated to meet the requirements of Industry 4.0. This ensures that they can work with new systems and share data in real time, which improves general operations.