ReeRisk –A Decisional Risk Engineering Framework for Legacy System Rejuvenation through Reengineering

Legacy systems are valuable assets for organisations. They continuously evolve with new emerged technologies in rapidly changing business environment. Web services, together with service-oriented architectures, facilitate legacy system webification and evolution in service-oriented computing environment. Traditional approaches to integrate legacy systems with Web service technology are wrapping a legacy system as a black-box without adequate system understanding. In this paper, we proposed a reengineering approach which applies an improved agglomerative hierarchical clustering algorithm to restructure legacy code and to facilitate legacy code extraction for Web service construction. It supports service identification and service packaging and archives legacy system migration into service-oriented architectures by providing functional legacy code as Web services.

The modernization of legacy software is a growing priority for U.S. enterprises that want to remain political or business competitive in a data-driven economy. Although legacy systems often serve a central role in an organization or department, they come with various burdens including high maintenance costs, low elasticity scalability, and a lack of interface capacity with modern technologies. Therefore, like many aspects of innovation and digital technology, Artificial Intelligence (AI) has the potential to transform legacy systems by allowing automated code refactoring, in system optimization, and process decision management. The authors present a comprehensive review of AI-enabled approaches to successfully and cost-effectively modernize legacy systems for enterprise applications. They highlight the need to provide organizations and enterprises with the ability to be adaptive or flexible in a sustained and cost-effective manner for a wide scope of legacy system modernization. Based on new literature in legacy systems using AI, the authors provide examples of applications in enterprise resource planning, smart manufacturing systems, cloud integration and migration, and multi-cloud optimization and cost savings activities. Also presented are frameworks and best practices for successful implementation for each of these new areas, and the opportunistic challenges of analysis complexity of integrated systems, integration to newer technological spaces, and systems knowledge or skills gaps. The data shows that while AI extends current functional capacity of legacy systems, it also calibrates legacy systems within current governance expectations, strategic outcomes for digital transformation, flexible scaling and strategies for secure cloud capabilities, and is the safest and most cost-efficient approach to modernizing legacy systems to enhance or to reduce enterprise risk.

Evolution and maintainability of legacy systems is all time attention drawing subject for researchers and especially practitioners. Discovering the crosscutting concerns and separating it from core functionalities of a software system may help in evolution of the legacy systems. Aspect-oriented software development (AOSD) tries to achieve the goal. AOSD is new programming paradigm which helps to bring in modularity in the program by writing the crosscutting concerns in the form of ‘aspects'. Modularity brings comprehensibility and hence maintainability of the software system. Tools and techniques, which aid in identifying the crosscutting concerns in such systems and refactoring them into aspects, are needed to apply aspect-oriented techniques to legacy systems at use in industry. This chapter aims to identify issues, problems and approaches used in the migration from legacy systems to aspect-oriented software system.

Iterative reengineering process is considered as an effective methodology to organize reengineering projects on degenerated but in-use legacy systems. By replacing legacy components gradually, the functionalities and requirements of legacy system can be satisfied. In practice, scheduling legacy components and data in every iterative cycle correctly is a great challenge to project managers, because of the complexities of dependency relations between legacy components and data. Formal Concept Analysis (FCA), which has strong mathematical capability of processing a group of elements with properties, is introduced to solve this issue. The major contribution of this paper is proposing an original Top-Down Schedule Algorithm (TDSA) based on FCA to compute an Iterative Cycle Reference Table (ICRT), which lists all potential cycles for an iterative reengineering project. The computed iterative cycles can be performed in any order without stopping legacy system.

In this paper, a Service-Oriented Software Reengineering (SOSR) methodology is proposed for reengineering a legacy system into a service-oriented system. Although Service-Oriented Computing (SOC) enables a software developer to design loosely coupled software components and integrate them with other software systems, most components in a legacy system were not developed as services. The SOSR methodology is based on a set of best practices that are architecture-centric, service-oriented, role-specific and model-driven. The SOSR methodology is demonstrated by the modernisation of two different legacy systems – a Business-to-Business (B2B) system and the other is a Business-to-Consumer (B2C) System. The resulting service-oriented systems and the evaluation of the methodology in terms of non-functional system requirements such as interoperability, etc. show that this methodology can be used by software developers and system integrators to reengineer tightly coupled legacy information systems into the loosely coupled, agile, service-oriented information systems.

Business rules are a set of conditional operations attached to a given data result. Over time, systems that implement business rules are updated when organizations change the data and operations to the changing business needs. On legacy systems, it is very difficult to extract business rules because of the inconsistency of documentation. So it is necessary to extract business rules from source code. Identifying domain variables is a significant step in extracting business rules from source code. This paper proposes a solution to identify domain variables automatically from source code by applying information-flow relations algorithm. The solution contains three steps: identifying variables affected by input domain variables; identifying variables, which affect output domain variables; domain variables management. It has been applied to a large complex financial legacy system, which has proved to be successful.

1980년대 이후, 소프트웨어 위기에 대응하기 위한 다양한 소프트웨어 공학적 기법들이 출현하기 시작 하였고, 현재 소프트웨어의 질적 항상 및 생산성 향상을 꾀하기 위한 개발 지침으로 소프트웨어 개발 프로세스를 적용하는데 관심이 집중되어지고 있다. 그러나 대부분의 방법론들이 앞으로 구축한 새로운 시스템의 재사용성 확보에 치중할 뿐, 기존의 레거시 시스템 자원을 재사용하여 새로운 시스템을 구축하는 재공학 방법은 지원하지 못하고 있는 실정이다. 본 논문에서는 레거시 시스템의 효율적 진화를 위한 재공학 프로세스(Reengineering Process)로 RUP를 확장, 커스터마이징하여 구성한 RUP+re을 제시한다. RUP+re는 Small-h 모델을 바탕으로 크게 역공학 워크플로우(Reverse Engineering Workflow), 변환 워크플로우(Transformation Workflow), 진화 워크플로우(Evolution Workflow)로 구성되어진다. RUP+re의 각 워크플로우와 세부스텝을 제공하고 재공학 사례 연구론 통하여 실질적으로 프로세스를 검증함으로서 레거시 시스템 자원의 진화론 위한 소프트웨어 재공학 프로세스 구축에 효율적인 지침을 제공한다. The various software engineering techniques have been come out in order to cope with the software crisis since 1980's. These days the software Engineering has focused on the process of software development which is the guide for a qualify and productive improvement of software. But, most of the methodologies assume that a new system will be constructed and reused in the future. these do not support how we reuse legacy system's resources and construct a new system efficiently. In this paper, we present an efficient Reengineering Process for legacy system evolutions, RUP+re, which extends and customizes RUP. RUP+re consists of Reverse Engineering Workflow, Transformation Workflow and Evolution Workflow based on Small-h model. We describe RUP+re's workflows with their own detail steps and verify the process through the case study practivally. So, we provide efficient guidelines to the software Reengineering process construction which evolutes the resources of a legacy system.

This article presents a comprehensive approach to migrating legacy systems to modern technologies like self -hosted Artifactory. It emphasizes understanding the legacy systems architecture, planning the migration meticulously, executing it efficiently, and engaging in post -migration activities for sustained success. The key phases include documentation review, codebase analysis, dependency mapping, goal definition, risk assessment, resource allocation, migration strategy determination, infrastructure setup, Artifactory installation, data migration, thorough testing, post -migration activities like monitoring, user training, feedback loop establishment, and maintenance. Success metrics encompass enhanced security, increased productivity, scalability advantages, cost savings, and improved performance. Visual aids, such as security risk distribution, scalability comparison, cost of ownership over time, and performance comparison charts, provide insightful data. The abstract underscores the significance of meticulous planning, execution, and ongoing management for successful migration, leading to improved performance, security, and scalability.

This work presents a risk assessment methodology, preliminary proposed in [1], which is the fusion of the absolute and the relative risk assessment methods adopted by the International Civil Aviation Organization. The proposed methodology uses the Fluid Stochastic Petri Net (FSPN) as modeling formalism, and compares the safety metrics estimated from the simulation of both the proposed and the legacy system models. It was applied to assess the safety properties of a new air traffic surveillance concept, named automatic dependent surveillance - broadcasting (ADS-B). As conclusions, the proposed methodology assured to assess the safety properties of systems based on the current safety critical system paradigm - especially concerning the air transportation system. Besides, the FSPN formalism provided important modeling capabilities and discrete event simulation allowing estimating the desired safety metrics. Finally, the ADS-B (proposed system) has significantly reduced the risks of separation losses between aircrafts if compared to the usual surveillance radar systems (legacy system) in air traffic control (ATC) environment.

From old to new: Assessing cybersecurity risks for an evolving smart grid

This paper describes a modernization effort to a critical subsystem onboard NASA's Enhanced MODIS Airborne Simulator (eMAS). A new Blackbody Temperature Controller subsystem greatly improves eMAS' infrared-imaging calibration and offers substantial gains in measurement and control performance. Reductions in Size, Weight and Power (SWaP) over prior instrument hardware are also achieved. Real-time control from the ground via the NASA Airborne Science Sensor Net is enabled by the ARM-based, embedded controller. A linear-drive stage delivers 140W peak power to a Thermoelectric Cooler (TEC) array for the blackbody. The paper details the rationale for selection of a linear power stage and corresponding analyses and prototyping that traded PWM versus linear methods. Linear drive is often considered grossly inefficient; a deeper analysis and novel packaging techniques indicate otherwise for this specific application. A survey of high-performance, temperature-measurement techniques reveals that new System-on-Chip (SoC) offerings substantially reduce complexity over prior state-of-the-art with little compromise in performance. Packaging volume of the new technology also radically shrinks. Lab results indicate measurement readout-noise (15mK, 3σ) improvements over legacy eMAS hardware. Selection of the temperature sensor and NIST-traceable calibration yields an overall systematic error of 60mK typical, 141mK worst-case. A full measurement system error budget is presented. The measurement stability aids performance and tuning of the control algorithm, also discussed. Two controllers have been built and integrated into the instrument in preparation for the upcoming deployment season. The new development is best understood in the context of the eMAS whiskbroom imager, its infrared capability, and the implementation of its blackbody infrared references. The blackbodies are built using circular plates of copper, coated with a special, high-emissivity paint that appears diffuse at infrared wavelengths. A pair of references on either side of the spectrometer's field of view provides an in-system, two-point calibration every scan line. An array of TEC modules is sandwiched between each plate and a forced-air cooled heatsink. A single sensor is read out at high precision with room for a coarse, auxiliary channel. Historically, temperature readout and control of these references were performed with a system mounted far from the spectrometer optical bench. Low-level analog signals were sent over long (10') cables in close proximity to the TEC array's high-current drive. This rendered the temperature measurement highly susceptible to systematic noise. The legacy system also utilized an analog control loop that could not be tuned or modified over time. The new controller leverages modern embedded system techniques to radically shrink the packaging volume and enable real-time control. Low-noise methods and clever thermal management allow the controller to be placed in close proximity to the reference plate. The overall result is an improvement in performance, reduction in system volume, and a drop in weight. Aircraft managers always welcome a weight reduction for airborne sensors; however, the eMAS technicians tasked with repeatedly installing the system are the real beneficiaries. Overall, the modernization effort represents a major step forward for this unique NASA Airborne Science asset.

: The approach that one chooses to evolve software-intensive systems depends on the organization, the system, and the technology. We believe that significant progress in system architecture, system understanding, object technology, and net-centric computing make it possible to economically evolve software systems to a state in which they exhibit greater functionality and maintainability. In particular, interface technology, wrapping technology, and network technology are opening many opportunities to leverage existing software assets instead of scrapping them and starting over. But these promising technologies cannot be applied in a vacuum or without management understanding and control. There must be a framework in which to motivate the organization to understand its business opportunities, its application systems, and its road to an improved target system. This report outlines a comprehensive system evolution approach that incorporates an enterprise framework for the application of the promising technologies in the context of legacy systems.

Legacy code in IT infrastructures poses significant challenges, including technological obsolescence, integration difficulties, scalability issues, and high maintenance costs. Effective management and modernization of legacy systems are crucial to prevent these outdated codes from hindering technological advancement and operational efficiency. This article examines the pervasive challenges associated with legacy code and proposes a multi-faceted approach to overcoming these issues. By adopting strategies such as incremental modernization, automation, refactoring, leveraging cloud technologies, and investing in workforce development, organizations can enhance the functionality of legacy systems within the modern IT landscape. These strategies not only mitigate the risks associated with legacy systems but also align them with contemporary business needs and technology standards.

The next generation automatic test system (NGATS) is the latest addition to the Integrated family of test equipment (IFTE) developed and managed by product manager, test, measurement and diagnostic equipment (PM TMDE). This is Version 6 of what is commonly known as the Base Shop Test Facility series of systems developed by the off platform, automatic test system program. NGATS is a reconfigurable, general-purpose, mobile, deployable, automatic test system designed to provide sustainment support to all Army weapon systems. This flexible testing platform will finally standardize and reduce the quantity of the Army's ATE systems to one modern tester and improve weapon system availability. It will allow for a decrease in logistical support requirements for ATE systems and the supported weapons systems and increase the mobility, deployability, affordability and supportability, of automated test equipment. NGATS assures compliance with Joint NxTest architecture and will support the agile rapid global combat support (ARGCS) advanced concept technology demonstration (ACTD) by using a common framework for DOD automatic test systems (ATS). Incremental deployment of this system will ultimately modernize and replace three existing test platforms, DSESTS, BSTF (V) 3, and BSTF (V) 5. NGATS will not only support the weapons systems currently fielded but will also support additional weapons systems as they are developed and fielded in the future. The NGATS will initially take advantage of the existing Test Program Sets for legacy weapons systems however new systems and those legacy systems that will remain in the Army inventory will have TPSs designed to take full advantage of the modern NGATS technology. Increment 1 replaces DSESTS and supports the weapons systems in the heavy brigade combat team (HBCT). The TPSs for Abrams, Bradley and other systems will be rehosted to NGATS with significant improvements. Increment 2 replaces BSTF (V) 3 and supports the MLRS, Avenger, Paladin, TOW, SINCGARS, Kiowa Warrior, Ground Based Sensor, G/VLLD, and M1A1 (SRUs) systems. Increment 3 replaces BSTF (V) 5 and supports the Kiowa Warrior and Apache. There has been increased interest in utilizing the IFTE NGATS as an Army organic sustainment support platform. Other systems that are candidates for support are KNIGHT, LLDR, STRYKER, FCS, Firefinder, LOSAT, NLOS, HIMARS, Land Warrior, SHORADS, TAD, and CROWS.

Legacy system modernization is being revolutionized by artificial intelligence, creating a paradigm shift in how organizations migrate from outdated architectures to contemporary frameworks. This article examines how AI-augmented modernization transforms the traditionally risky, costly, and complex process of updating enterprise systems through intelligent automation and machine learning. The integration of AI capabilities across the modernization lifecycle—from initial assessment and planning through code transformation, testing, and deployment—delivers substantial improvements in accuracy, efficiency, and business outcomes. By analyzing comprehensive case studies from financial services and healthcare sectors, the article demonstrates how AI-driven techniques dramatically reduce implementation timelines and costs while simultaneously improving quality and mitigating risks. Machine learning algorithms excel at extracting embedded business rules, identifying undocumented dependencies, automating code translation, generating comprehensive test cases, and predicting potential failure points before they impact operations. The emergence of augmented intelligence—where human expertise is amplified rather than replaced by AI—represents the next evolution in this field, enabling continuous modernization rather than traditional point-in-time projects. The economic implications are substantial, with organizations leveraging AI-augmented approaches experiencing significantly higher returns on investment and accelerated time-to-market for new capabilities. As legacy systems continue to age across industries, AI offers a promising path to addressing mounting technical debt while enabling the innovation necessary for competitive advantage in increasingly digital markets.