Software Assets Research Articles

Navigating Software Compliance and Optimization: A Comparative Analysis of Software Asset Management in ServiceNow and BMC Helix

Abstract: This research paper presents a comprehensive comparison of the Software Asset Management (SAM) applications offered by ServiceNow and BMC Helix. The study examines key features, software discovery and inventory capabilities, license management, compliance tracking, optimization strategies, and integration with other ITSM processes. By evaluating their strengths and weaknesses in managing software assets, this paper aims to provide insights for organizations seeking to optimize their software investments, ensure compliance, and reduce overall IT costs.

Digital Transformation in IT Asset Management Process

Information Technology Asset Management (ITAM) is a key process in the Information Technology (IT) sector. Since the late 20th century, maintaining a growing number of IT assets has become a challenging task. It requires technological advancements to streamline ITAM processes efficiently and cost- effectively. The digital transformation of ITAM processes can help organizations overcome many challenges. Digitalizing asset management processes requires a centralized repository for efficiently managing IT assets, such as workstations, laptops, and even software assets. This enhances security and ensures that assets adhere to security compliance. Technologies for digital transformation, alongside techniques such as process automation and the adoption of advanced Radio Frequency Identification (RFID) technology, can be utilized to improve ITAM. These technologies and techniques enable organizations to improve their asset management processes, meeting the organization's needs. This paper addresses the impact of digital transformation on IT Asset Management and explores areas for process improvement. Digital transformation impacts Asset Management by enabling faster asset- related decision-making, optimizing asset utilization, reducing maintenance costs, and providing greater transparency and asset traceability.

The Role of Artificial Intelligence in Enhancing Software Asset Management and License Compliance

The Role of Artificial Intelligence in Enhancing Software Asset Management and License Compliance

Decision Making on a Software Upgrade or Decommission with Data Mining and Machine Learning Techniques in Information Technology Industry

The Organizations have been investing more in Technology and Infrastructure spends like software upgrades, software renewals, software replacements, platform migrations etc., apart from investment in Business, People, and Processes. In this context, it is not an easy task for stakeholders to decide whether to go for a software upgrade or to replace it with another software. There is no unified approach or solution to consolidate data and relationships of Information Technology Assets, Software Upgrades, Software costs, Software defects, Software Performance Metrics, Security issues, IT system versions, service level objectives etc. Due to this, the decision making of software upgrades and software decommissioning is a tedious process and takes more time and effort. There is a need to build a solution that can integrate and validate the information like software assets, software upgrade success and failure likelihoods, cost benefit analysis of Cloud Computing, software metrics for fault prediction, software maintainability prediction results, Digital Transformation readiness and other related factors. There is an opportunity to apply Machine Learning techniques in defining and deriving the success likelihoods on the following data: Systems and data integration, software assets compatibility, operational service level agreement breaches, quality assurance metrics, security issues, number of open defects, number of defect fixes, number of priority incidents, mean time to resolve critical incidents, expected cost increase in software maintenance, potential cost reduction with the software or hardware replacement etc. This Research Proposal outlines the above mentioned to build a recommendation system aka decision tree namely Software Upgrades or Decommissions Life Cycle.

A Software Life-Extending Outsourcing Model: An Ethnographic Action Research of a Product Transformation for a European Telecom Equipment Manufacturer

Maximizing returns on software development costs have been a prime objective of product organizations. As the products mature, the organization explores alternatives to extend an existing product's life or retire it from its product portfolio. This article examines the changing trends of the outsourcing model for software product organizations based on a product's lifecycle and technology maturity. Adopting an ethnographic action research methodology, we study the journey of a software product's (re)aligning outsourcing strategy while evolving a parallel strategy for retiring products by either shifting focus from fixed to variable costs or monetizing them as “software assets.” As part of the conclusion, a software life-extending outsourcing model has been proposed based on the specific phase of product development and technology maturity. The article includes research implications for practice and academia in strategizing outsourcing based on a product's development phase.

Towards a Method to Enable the Selection of Physical Models within the Systems Engineering Process: A Case Study with Simulink Models

The use of different techniques and tools is a common practice to cover all stages in the development life-cycle of systems generating a significant number of work products. These artefacts are frequently encoded using diverse formats, and often require access through non-standard protocols and formats. In this context, Model-Based Systems Engineering (MBSE) emerges as a methodology to shift the paradigm of Systems Engineering practice from a document-oriented environment to a model-intensive environment. To achieve this major goal, a formalised application of modelling is employed throughout the life-cycle of systems to generate various system artefacts represented as models, such as requirements, logical models, and multi-physics models. However, the mere use of models does not guarantee one of the main challenges in the Systems Engineering discipline, namely, the reuse of system artefacts. Considering the fact that models are becoming the main type of system artefact, it is necessary to provide the capability to properly and efficiently represent and retrieve the generated models. In light of this, traditional information retrieval techniques have been widely studied to match existing software assets according to a set of capabilities or restrictions. However, there is much more at stake than the simple retrieval of models or even any piece of knowledge. An environment for model reuse must provide the proper mechanisms to (1) represent any piece of data, information, or knowledge under a common and shared data model, and (2) provide advanced retrieval mechanisms to elevate the meaning of information resources from text-based descriptions to concept-based ones. This need has led to novel methods using word embeddings and vector-based representations to semantically encode information. Such methods are applied to encode the information of physical models while preserving their underlying semantics. In this study, a text corpus from MATLAB Simulink models was preprocessed using Natural Language Processing (NLP) techniques and trained to generate word vector representations. Then, the presented method was validated using a testbed of MATLAB Simulink physical models in which verbalisations of models are transformed into vectors. The effectiveness of the proposed solution was assessed through a use case study. Evaluation of the results demonstrates a precision value of 0.925, a recall value of 0.865, and an F1 score of 0.884.

Towards Automation of the FORM/BCS Method

The software industry is facing more complex computer systems, with short development and sustainability issues. To deliver good software with these constraints, software reuse has become a central concept for minimizing design and realization costs. This study improves upon Feature-Oriented Reuse Method with Business Component Semantics (FORM/BCS), a software development method that produces adaptable architectures from reusable domain components. This is a promising method for reusable software assets and model creation. The objective of the FORM/BCS is to bring the industrial production chain to the software. This study proposes a model to automatically transform the FORM/BCS business subsystem component into a process business component. Two metamodels for business subsystems and process business components were developed. In addition, this study establishes correspondences between the source metamodel and target metamodel classes, transformation rules, and the instance of the source metamodel and generates the target metamodel instance. Detailed findings can help practitioners reduce software design costs and development time, and contribute to the advancement of knowledge in software engineering.

PARETO PRINCIPLE TO IMPROVE ANOMALY DETECTION ON SOFTWARE ASSET MANAGEMENT

Software Asset Management (SAM) is essential for a large company with a centralized software distribution system. Unfortunately, the operationalization of SAM has various problems. These problems become even more complicated when it comes to managing big data. This research proposes the Pareto Principle to reduce data dimensions to solve the problem of large data sizes without losing dataset characteristics before conducting anomaly detection. This anomaly detection is mandatory to identify and reduce invalid data due to misalignment or misclassification. Therefore, this study compares the state-of-arts anomaly detection algorithms: I-forest, KNN, and SVM. As a result, we found that SVM is the best algorithm, with an accuracy rate of 78.4%. In addition, using Pareto for the total population and software name variation effectively reduces the number of observations to 20% data instances with only 16.5% features without compromising the dataset characteristics. In the best algorithm based on experimental results, the use of Pareto increases accuracy by 3.2% and processing time efficiency by 20%.

Design of embedded software architecture for spacecraft electric propulsion system

With the rapid growth of the number of spacecraft tasks and the complexity of the system, spacecraft software development is faced with the challenges of high-quality and rapid delivery, increased complexity, and frequent changes in requirements. The reuse rate of software assets needs to be improved urgently. In order to realize cross-platform and cross-project reuse of software design, this paper studies the design of electric propulsion software architecture. Carry out requirements decomposition and subsystem division of electric propulsion systems for different platforms, extract common requirements and variability requirements at the system level and subsystem level based on the Feature-Oriented Domain Analysis method, and establish a requirements model. Based on the requirements model, an electric propulsion software architecture integrating “event-driven + components” is proposed, and the classic event-driven model of “publish-subscribe” is adaptively improved. The architecture has been instantiated and applied to 16 projects on 4 platforms, and the reuse rate of core assets has reached 70%. This paper has certain guidance and reference significance for spacecraft software asset reuse and platform construction.

A Method for Managing Software Assets in the Automotive Industry (Focusing on the Case of Hyundai Motor Company and Parts Makers)

We propose a method for managing software assets in the automotive industry to enhance software competitiveness and to reduce development costs. The ownership of software assets in the automotive industry is held by automotive parts companies, making it challenging to exchange these technologies. Moreover, the criteria for determining software assets are often unclear, resulting in difficulties in integrating automotive software and implementing over-the-air updates. To address these issues, we suggest breaking down black-boxed software assets into tradable components, valuating them, and introducing the concept of exchanging software technology assets. Additionally, we provide a structured approach for recycling used software assets and establish a software asset management system for registration and tracking. Our proposed approach can help traditional automotive OEMs narrow the technology gap with automakers such as Tesla and improve their software competitiveness in the automotive industry. This paper contributes to the advancement of software asset management practices in the automotive industry, and provides insights into the integration of automotive software and over-the-air updates.

TSDroid: A Novel Android Malware Detection Framework Based on Temporal & Spatial Metrics in IoMT

In the era of smart healthcare tremendous growth, plenty of smart devices facilitate cognitive computing for the purposes of lower cost, smarter diagnostic, etc. Android system has been widely used in the field of IoMT, and as the main operating system. However, Android malware is becoming one major security concern for healthcare, by the serious threat for our medical software assets, like the leakage of private information, the abusing of critical operations, etc. Unfortunately, the existing methods focus on building sustainable classification models, without fully considering system API which is the key to model aging. Compared to the traditional methods, we apply the lifeCycle of API as temporal metric. In addition to the temporal view, the “sizes” of the APPs are utilized as spatial metric in the spatial view. Based on this, we firstly discuss the temporal and spatial metrics together in terms of clustering, and then propose our novel framework-TSDroid. In this framework, we use TS-based clustering algorithm to obtain clustering subsets to enhance the detection capability. We have carried out an experimental verification on three existing excellent methods (i.e., Drebin, HinDroid, and DroidEvolver) and obtain good promotion effects by our framework.

Debugger for Declarative DSL for Telecommunication

Development of telecommunication product lines is still a very labor-intensive task, involving a great amount of human resources and producing a large number of development artifacts — code, models, tests, etc. Declarative domain-specific languages (DSLs) may reasonably simplify this process by increasing the level of abstraction. We use the term “declarative” implying that such a DSL does not enable the development of a closed software application, but rather supports creation, generation and maintenance of various kind of software assets — product database, events and event handlers, target code data structures, etc. At the same time, such a DSL may have some executable semantic, but it could be very specific and have many environment-wise requirements. Thus, execution and debugging of such DSL specifications is a meaningful task, which has no common solution due to the unique executable semantic. Consequently, it is not possible to use debug facilities of known DSL environments, such as xtext, MPS, etc. for such a case. In the current paper, we present a debugger for DevM — a declarative DSL intended for support device management in software development in the context of a router product line by a large telecommunication company. We clarify executable semantic for DevM, making it possible to execute DevM specifications in an isolated environment, i.e. in simulation mode, without generation of target code. We use a graphic model-based notation to depict every step of execution. Finally, we implement and integrate the debugger in the DevM IDE, using Debug Adapter Protocol and language server architecture combined with the Eclipse xText/EMF tool chain.

MU-math: educational game to improve students’ mathematical reasoning

Learning media are tools utilized to support the interaction of students with educators and learning resources in the implementation of the teaching and learning process. This development research uses the Thiagarajan or 4-D model with four stages: defining, designing, developing, and disseminating. The subjects of this research were grade VIII students of SMP Negeri 1 Umbulsari. The analytical method is validity analysis based upon validation by two lecturers of the Mathematics Education Study Program, FKIP University of Jember, and one mathematics teacher from SMP Negeri 1 Umbulsari. Practicality analysis was based on user response questionnaires and interviews, and effectiveness analysis using pretest/pretest and final test/posttest. This study uses the educational Game Mu Math on numbering pattern material to improve students' mathematical reasoning with the help of Unity software. MU Math Game is a PC (Personal Computer) based Game developed using Unity Engine Software and Lego Assets Tools. The MU Math game is an Adventure Open world type where players can explore through the maps provided. There are two levels and one mathematical reasoning to test students' mathematical reasoning abilities.

İşletmelerde Yazılım Lisanslarının Yönetiminde Risklerin İncelenmesi ve Fayda-Maliyet Analizi

In this study, a proposal on how software license assets should be managed with an enterprise risk management approach has been put forward. The study examined licenses, which mean software usage rights, and various license models with a complex structure. Within the scope of the study, the risks that might arise concerning software licenses were determined and these risks were categorized. Considerable risks prioritized by senior management in businesses were identified and responses and strategies were set out for each risk factor that needs to be kept under control. The method suggested in the study was applied in a large-scale enterprise acting in the telecommunications sector. As part of this application, the enterprise’s current situation in software asset management was revealed. The outcomes of the application are expected to be beneficial in determining the risk models of the enterprises. The study presents the study's management of software assets, determination of risks, and solutions to be applied to risks.

Sources of transport sector labor productivity performance in industrialized countries: Insights from a decomposition analysis

This study examines patterns and sources of growth in the transport sector's labor productivity across 13 industrialized countries over the period 2000–2015. Through decomposition analysis – specifically, the growth accounting method and industry origin analysis – the study makes three principal findings. First, total factor productivity growth plays a crucial but often underleveraged role in driving the sector's growth and catch-up on labor productivity. Second, digital transformation – especially investment in software and database assets – is a significant source of the sector's labor productivity growth and catch-up, while innovation-related capital has a complementary effect. Third, subsector-level labor productivity improvement is the major driver of the sector's overall labor productivity growth, while the growth contribution of labor reallocation among subsectors is insignificant. The article concludes with a discussion of policy insights and implications.

Facilitating Software Reuse Through Design Characteristics in Object-Oriented Paradigm

Software are used to increases quality as well as productivity that could be associated with lower costs. Implementing software with capability for reuse is difficult without sufficient support. This paper includes tutorial represents four important design characteristics in object-oriented paradigm for facilitating the production of more reusable software. It reviews the concepts of modularity, cohesion, coupling and information hiding and provides a discussion about their effect on software reuse. Object-oriented approach is having wide attention both in research environments and in industry. This tutorial is prepared for newbie developers of reusable software assets and for everyone else interested in the subject.

Intelligent and compliant dynamic software license consolidation in cloud environment

Based on the virtualization technology and pushed by the softwarization paradigm and the actual demand for services and resources, commercial cloud data centers know an unprecedented expansion. The systematic presence of software and the services generated enabled the development of the already dense application expenses. This causes, not only a cost explosion, especially when proprietary solutions protected by licenses are in hand, but also, represents a critical need in terms of software asset and resource management at the SaaS level. In addition to these costs, inefficient resource utilization, and the resulting energy represent an important part of the operational expenditure of data centers and are still a hot topic despite the consolidation initiatives put in place. The main objective of the consolidation service is to maximize resource exploitation while minimizing energy consumption and costs, among others. Even so, we have noticed that the reported literature doesn’t treat license management in the cloud environment as a whole, especially, from the resource management perspective and the overwhelming majority of the consolidation work focuses on resource optimization at the IaaS level. Therefore, we propose a reinforcement learning-based scheme that allows efficient use of resources and optimizes costs, energy consumption, and resource wastage, while remaining compliant. The experimental results show that our intelligent consolidator outperforms the baseline approaches according to the evaluation metrics used regardless of the resource heterogeneity and the data center dimensionality.

Visualizing the customization endeavor in product-based-evolving software product lines: a case of action design research

Software Product Lines (SPLs) aim at systematically reusing software assets, and deriving products (a.k.a., variants) out of those assets. However, it is not always possible to handle SPL evolution directly through these reusable assets. Time-to-market pressure, expedited bug fixes, or product specifics lead to the evolution to first happen at the product level, and to be later merged back into the SPL platform where the core assets reside. This is referred to as product-based evolution. In this scenario, deciding when and what should go into the next SPL release is far from trivial. Distinct questions arise. How much effort are developers spending on product customization? Which are the most customized core assets? To which extent is the core asset code being reused for a given product? We refer to this endeavor as Customization Analysis, i.e., understanding the functional increments in adjusting products from the last SPL platform release. The scale of the SPLs’ code-base calls for customization analysis to be conducted through Visual Analytics tools. This work addresses the design principles for such tools through a joint effort between academia and industry, specifically, Danfoss Drives, a company division in charge of the P400 SPL. Accordingly, we adopt an Action Design Research approach where answers are sought by interacting with the practitioners in the studied situations. We contribute by providing informed goals for customization analysis as well as an intervention in terms of a visual analytics tool. We conclude by discussing to what extent this experience can be generalized to product-based evolving SPL organizations other than Danfoss Drives.

Foundations for a Fission Battery Digital Twin

As the nuclear industry moves toward construction of microreactors and next-generation reactors, these efforts pose new challenges. A digital-twin tool will reduce costs and risk through integration of the disparate systems used in the design, construction, and operation of these reactors. Recent investments at Idaho National Laboratory (INL) in open-source digital engineering and multiphysics framework development provide a foundation from which to create and evaluate a digital twin for nuclear reactors. This digital-twin tool will use the Single Primary Heat Pipe Extraction and Removal Emulator (SPHERE) and Microreactor AGile Non-nuclear Experimental Testbed (MAGNET) as case studies to develop a digital twin with both single and 37 heat pipe test articles. The digital twin will provide the capabilities of remote monitoring and unattended operation (autonomous control) of these systems. A digital twin is a digital replica of an operating asset that can display data received from live sensors, update a physics model for the asset with the received data, compute predictive results of operational status with artificial intelligence (AI) to aid in optimizing asset use, and apply asset control accordingly. This twin will be developed through integration of the open-source technologies Deep Lynx (a data-warehouse technology) and the Multiphysics Object-Oriented Simulation Environment (MOOSE), physical-asset sensors, and physical-asset controls. Specifically, the general AI will successfully predict the events described as MAGNET heat pipe article test cases (such as heat pipe failure) using integrated data from the MAGNET sensors and physics-based models, including developed meta models. The integration of open-source INL software and AI assets with sensor data from a test bed will lead to a repeatable framework and guide for the creation of future digital twins. The team will also perform AI model training and experimentation to determine what models and features are most important to enable intelligent, autonomous control as well as to evaluate and determine best practices for digital-twin cybersecurity.

Helping Young Children Develop Emergent Language and Literacy Skills: The Roles of Curriculum, Training, and Coaching

This article discusses the need for accessible, practical, and cost-effective professional development for Head Start teachers in developing emergent language and literacy skills. We describe the development and components of ABCDuet: teaching materials, professional development and personalized coaching. The program is designed to make training and coaching in language and literacy teaching practices more effective, accessible, user-friendly, and sustainable, using innovative software and video assets. We report findings on the feasibility and efficacy of ABCDuet implementation on teacher and child outcomes from an evaluation of the program in 21 Head Start classrooms. Outcome data indicate the program can be implemented in authentic preschool settings by teachers with varied educational backgrounds and experience, serving children with a wide range of emergent language and literacy skills.