Software Engineering Techniques: Design for Quality

Replication has become a fundamental requirement for distributed systems in order to avoid network overload as well as to provide fault tolerance. The objective of this work is to show how replication can be supported in an abstract and technology independent way. The work presented in this paper is called Ambient-PRISMA. Ambient-PRISMA combines PRISMA and Ambient Calculus. PRISMA is an approach for developing aspect-oriented software architectures. Ambient Calculus is a formalism that provides primitives for specifying distribution, mobility and replication. In this way, both the advantages of PRISMA and Ambient Calculus are obtained.

Software risk management is a critical and multi-stage process. All over the world, IT Industries face some threats during software risk management processes. Bangladesh is not exceptional. Our principal goal is to manage risk for Bangladesh's IT Industry. To gain a clear and transparent idea survey is the most effective way. And so, we arranged a survey questionnaire and collected data for risk impact areas on Bangladesh IT Industry. A workable and feasible risk management approach prompts an idea for the risk-mitigating plan. In any case, the high proportion of IT project failures demonstrates the futility of risk mitigation activities. From the survey, it is discovered that the most irritating obstructions behind software disappointment for the presence of covered-up and inconspicuous risks and lack of user communication, and lack of proper training on new technology which is overlooked in the greater part of the models. The proposed model works with the improvement of the risk mitigation plan through four phases, the DVC committee, and the NUT train-up team. Depending upon the survey replies added another unique feature called the NUT train-up team. The model considered all four phases of risk management, with the mitigation phase and training on new technologies receiving the greatest attention.

Software maintenance is an ongoing process during the lifetime of a software system. Once it is on production the software application must evolve when new anomalies uncovered or when new user requirement arises. Prior to any software evolution, decision making need to be undertaken by the organization's Software Maintenance Decision Maker (SMDM) whether to proceed with the evolution required and how to proceed with the required maintenance. However, the study on decision making in software maintenance is very limited. Existing researches focus mainly on project management, software effort estimation or decision making on software maintenance policy selection. The uniqueness of this study relies on the ability of knowledge management method to capture the software maintenance knowledge of a software application that is inherent in the Software Application Expert Maintainer (SAEM) mind and software application specification and use it to support the software maintenance analysis phase. It is crucial for the knowledge to be available for the SMDM especially in the event that the expert software maintainer is not available. This study is also able to assist Software Application Beginner Maintainer (SABM) to gain maintenance knowledge of a particular software application that is available in the knowledge-based decision making model. This results in shorter time to learn on how to maintain a software application without the SAEM assistance. This paper proposes an alternative to the SAEM and software application specification knowledge through the Maintenance Decision Making Knowledge-Based (MademKB) Model that can preserve the maintenance knowledge of a software application to support software maintenance decision making. The MADEMKB is evaluated using case study with Malaysian organization.

A methodology for forecasting knowledge work projects

While currently, general attention is focused on generative AI applications such as ChatGPT and their impact on project management; there is hardly any talk of blockchain applications. Using a bibliometric analysis of the pertinent literature, we can show a significant increase in the application of blockchain technology to project management and a clear link between AI, blockchain technology, and project management. Our research shows not only a significant increase in publications in the field of the application of blockchain technologies in project management, particularly in the construction industry and in China, but also the first concrete examples of application in the area of contract management, smart contracts, secure payment transactions as well as data management. The results of our research show in a unique way the direct connection between AI and blockchain technologies and their application in project management using the example of smart contracts with secure payment transactions in plant construction. This opens up a fascinating field of research and provides practitioners with exciting insights into an innovative application for AI and blockchain technologies.

The management of a project portfolio is a complex decision process because it encompasses the achievement of multiple objectives. A critical point that increases the complexity in the decision-making process of a portfolio manager is the allocation of human resources to manage the projects of the portfolio, project managers, which is crucial to the organization’s performance. In this case, the project manager can manage more than one project simultaneously and it is necessary to assign project managers to the projects, considering that project activities have an amount of work to be accomplished. The main objective of this work was to provide support for this capacity management problem, which aims to provide an easier decision-making process for the capacity management of an industrialization project portfolio. Therefore, it was developed: a hybrid model that creates a schedule respecting the resource constraints and the established due dates; a recommendation system that considers project managers’ allocation and projects requirements; and, an automatic status report that allows identifying the project portfolio capacity usage.

Governments often take on the duty for implementing mass transportation systems, complex projects that gather a range of singular challenges that are multiplied within the governmental context, ruled by specific laws and codes. This research analyzed the project technical supervision by the main state-owned company linked to the Government of the State of So Paulo for the implementation of these works: The Companhia do Metropolitano de So Paulo, exploring and discussing its problems and proposing improvements, also, discussing the influence of lowest price biding and the phenomena entitled in this research as the claim and change orders culture over the activity of technical supervision. Through qualitative interviews with technical supervision professionals, their work and perceptions were contextualized, described and analyzed. It was noted that the need for project management is inherent to the technical supervision activity. It was found that the work of these professionals was, in general, done with little standardization, varying from team to team and even between professionals in the same teams. The work was performed proactively by these professionals, especially with regard to the inspection of the scope quality, the activity that showed greater standardization. It was noted that there was no standardization in the supervision of construction schedules and significant lack of focus on long term contractual schedules by these teams. Regarding cost control, it was found that the activity is often carried out in duplicity, however with a focus on contract costs, and not the scope itself, reflecting the need to meet government audit bodies rules and not the project management itself. It was observed that the technical supervision activity was carried out according to the required by these audit bodies and by the current legislation, and it was observed that the need to meet those requirements negatively impacts the project management and the project implementation process. Several approaches have been proposed to the improvement opportunities observed, mostly in line with the company approach to the same problems, which also diagnosed the situation along the research period, through the works of the Project Management Committee, contained in the Engineering and Planning Department. Through a working group for the standardization of the activity, the technical supervision processes were divided into three main classes: Quality and scope, schedules and costs. These the group developed integrated process for each class, innovating the project management in the company. Additionally to the company program, it was proposed improvements referring to the work of technical supervision teams, the use of digital systems for their activity and use of a unified and accessible repository for the information's registry and data management in the company projects, points that showed improvement need. Finally, the influence of lowest price biding and the claim and change orders culture were verified as phenomena, as well as their influence over the technical supervision activity. The low price offered in the bidding process interferes with the implementation process, often resulting in slow pace progress of the contractors, who eventually start trying to influence the modification of the contract, seeking change orders and claims that can provide financial viability to the enterprise, whereas modifying the technical supervision activity and the client-contractor relationship, causing a high level of formalization that does not benefit the project performance.

'Research through innovation' is the current demand echoing throughout the healthcare industry, healthcare institutions tend to invest heavily in technology. Data Science being the major disruptor across industries is being incepted through establishment of innovation and R&D centers within their respective organizations. Data Science has become a critical component for the healthcare industry, supporting innovative approaches towards advanced clinical practice, clinical research and corporate management, serving to build an intelligent enterprise. Every healthcare institution maintains a good number of technical staffs with IT, Software, data management, BI and analytical capabilities, aiding the institutions to manage report and publish its data in some or the other way, grossly covering most aspects of data science knowingly or unknowingly. Setting up a new entity within the organization by recruitment of staff with Data Science based skill sets would be the first thought to strike the management, which in contrast would end up as disaster when it comes to understanding the organizational culture, processes, infrastructure, platforms, data etc. Hence in order to setup a data science hub, regrouping or realigning some of the existing institutional resources is crucial. With this approach, the Data Science hub would carry out three primary functions. The "Project Management & Data Sourcing", the "Data Management & General Analytics" and "Advanced Analytics". Current resources can be reorganized within the first two functions, further; it would be about establishing an advanced analytics group within the hub which would perform the Machine learning and AI functions.

A Knowledge Cube, or cube for short, is an intelligent and adaptive database instance capable of storing, analyzing, and searching data. Each cube is established based on semantic aspects, e.g., (1) Topical, (2) Contextual, (3) Spatial, or (4) Temporal. A cube specializes in handling data that is only relevant to the cube's semantics. Knowledge cubes are inspired by two prime architectures: (1) Dataspaces that provides an abstraction for data management where heterogeneous data sources can co-exist and it requires no prespecified unifying schema, and (2) Linked Data that provides best practices for publishing and interlinking structured data on the web. A knowledge cube uses Linked Data as its main building block for its data layer and encompasses some of the data integration abstractions defined by Dataspaces. In this paper, knowledge cubes are proposed as a semantically-guided data management architecture, where data management is influenced by the data semantics rather than by a predefined scheme. Knowledge cubes support the five pillars of Big Data also known as the five V's, namely Volume, Velocity, Veracity, Variety, and Value. Interesting opportunities can be leveraged when learning the semantics of the data. This paper highlights these opportunities and proposes a strawman design for knowledge cubes along with the research challenges that arise when realizing them.

Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. It has become common practice to make extensive use of high-throughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for scalable data management and easily accessible means of analysis.We present qPortal, a platform providing users with an intuitive way to manage and analyze quantitative biological data. The backend leverages a variety of concepts and technologies, such as relational databases, data stores, data models and means of data transfer, as well as front-end solutions to give users access to data management and easy-to-use analysis options. Users are empowered to conduct their experiments from the experimental design to the visualization of their results through the platform. Here, we illustrate the feature-rich portal by simulating a biomedical study based on publically available data. We demonstrate the software’s strength in supporting the entire project life cycle. The software supports the project design and registration, empowers users to do all-digital project management and finally provides means to perform analysis. We compare our approach to Galaxy, one of the most widely used scientific workflow and analysis platforms in computational biology. Application of both systems to a small case study shows the differences between a data-driven approach (qPortal) and a workflow-driven approach (Galaxy).qPortal, a one-stop-shop solution for biomedical projects offers up-to-date analysis pipelines, quality control workflows, and visualization tools. Through intensive user interactions, appropriate data models have been developed. These models build the foundation of our biological data management system and provide possibilities to annotate data, query metadata for statistics and future re-analysis on high-performance computing systems via coupling of workflow management systems. Integration of project and data management as well as workflow resources in one place present clear advantages over existing solutions.

Software project risk analysis using Bayesian networks with causality constraints

Software risk management can be defined as an attempt to formalise risk oriented correlates of development success into a readily applicable set of principles and practices. Earlier research suggests that it can reduce the likelihood of a system failure. Using a survey instrument we investigate this claim further. The investigation addresses the following questions: (1) which characteristics of risk management practices; and (2) which other environmental and process factors (such as development methods, manager's experience) relate to improved performance in managing software risks? Our findings support in general the claim that the use of risk management methods improves system development performance. Yet, little support was found for the claim that specific risk management methods are instrumental in attacking specific software risks. Overall, risks are better managed with combined measures of having experienced project managers, selecting a correct project size, and investing in and obtaining experience in risk management deliberations.

Background: The integration of digital solutions is a cornerstone of modern integrated care, enabling enhanced coordination, communication, and personalized care across health systems. Digital health innovations are increasingly recognized as crucial for improving the efficiency and quality of care delivery, reducing fragmentation, and empowering patients to take an active role in managing their health (1). The International Foundation for Integrated Care (IFIC) emphasizes the significance of digital solutions in pillar 7 of the nine pillars of integrated care. The need for structured, effective data management frameworks is underscored by the complexity of modern health systems, where digital solutions must comply with evolving data protection laws such as the General Data Protection Regulation (GDPR) and emerging requirements like the AI Act. Without robust data governance, these systems face challenges in ensuring privacy, security, and ethical data use while striving to achieve integrated care goals. Approach: The "Integrating Data in Integrated Care" (IDIC) methodology, developed during the EU funded ValueCare Project (https://projectvaluecare.eu/), responds to these challenges, offering a structured reference model for data governance that aligns with European legislation. The IDIC methodology is organized around the data life cycle of digital health projects, addressing the specific data management needs of Integrated Healthcare systems (IHC). The IDIC methodology responds to two major challenges: 1.Privacy, ethical, and legal requirements: Ensuring GDPR compliance, defining clear roles for data governance, safeguarding against bias, and implementing privacy-by-design principles. 2.Patient and technology-centered challenges: Addressing data fragmentation, enhancing data quality, and empowering patients by giving them control over their data, all of which are critical to the success of digital health solutions. IDIC organizes data management into two core categories: 1.Project Design, Dissemination, and Knowledge Data: Including co-design data from focus groups; dissemination and communication data, including databases with stakeholders’ details and knowledge produced during the project (i.e. intervention models, algorithms). 2.Patient Data in IHC Pilots: Including patient personal data (i.e demographic data gathered during recruiting phases), medical devices data (collected from monitoring devices), wellbeing data from personal devices (pedometers, apps, etc.)., interaction data (self-input data, interactions with chatbots, preferences, etc.), implementation data (record keeping, appointments, coordination meetings, etc). For each category, the methodology emphasizes: Data size and format; Storage and backup strategies; Retrospective or prospective datasets organisation; Different roles of individuals and organisations involved in data management; Compliance with the FAIR (Findable, Accessible, Interoperable, Reusable) principles; Strategies for consent, data sharing and multi-center pilot implementation; Ethical and legal issues; Long term preservation of data; Results and implications: The IDIC framework provides a replicable, GDPR-compliant approach for data management in IHC projects. IDIC ensures that patient rights are preserved while supporting the deployment of digital solutions, while promoting the IHC agenda. IDIC offers a valuable tool for the digital transformation of health systems, aligning with the broader goal of integrating care through advanced data governance practices.

Good accessibility of publicly funded research data is essential to secure an open scientific system and eventually becomes mandatory [Wellcome Trust will Penalise Scientists Who Don’t Embrace Open Access. The Guardian 2012]. By the use of high-throughput methods in many research areas from physics to systems biology, large data collections are increasingly important as raw material for research. Here, we present strategies worked out by international and national institutions targeting open access to publicly funded research data via incentives or obligations to share data. Funding organizations such as the British Wellcome Trust therefore have developed data sharing policies and request commitment to data management and sharing in grant applications. Increased citation rates are a profound argument for sharing publication data. Pre-publication sharing might be rewarded by a data citation credit system via digital object identifiers (DOIs) which have initially been in use for data objects. Besides policies and incentives, good practice in data management is indispensable. However, appropriate systems for data management of large-scale projects for example in systems biology are hard to find. Here, we give an overview of a selection of open-source data management systems proved to be employed successfully in large-scale projects.

In 1994 the American Academy of Orthopaedic Surgeons established an outcomes initiative. The Musculoskeletal Outcomes Data Evaluation and Management System had two goals: (1) to create validated patient-based functional health questionnaires that would become the gold standard for musculoskeletal research, and (2) to collect data from practicing orthopaedists using these instruments. We assessed the adequacy of the hip and knee portion of the Musculoskeletal Outcomes Data Evaluation and Management System data collection process to learn how to improve these processes. Database elements included demographic and clinical information and health and well-being scales. Only 715 records or 578 patients of the initial 2419 records (30%) had complete baseline and followup information for either hip or knee replacement procedure. Only 17% of the patients who had a knee replacement and 20% of the patients who had a hip replacement returned for followup between 7 and 9 months. The Musculoskeletal Outcomes Data Evaluation and Management System achieved its goal regarding the development of validated questionnaires, but the data collection component did not. The project's implementation and management were flawed. This experience offers the field of orthopaedics valuable lessons that can be applied to future large-scale data collection efforts.