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Context: Domain-Driven Design (DDD) has gained significant attention in software development for its potential to address complex software challenges, particularly in the areas of system refactoring, reimplementation, and adoption. Using…
As testified by new regulations like the European AI Act, worries about the human and societal impact of (autonomous) software technologies are becoming of public concern. Human, societal, and environmental values, alongside traditional…
In this work, we reflect on the data-driven modeling paradigm that is gaining ground in AI-driven automation of patient care. We argue that the repurposing of existing real-world patient datasets for machine learning may not always…
The continuous software engineering paradigm is gaining popularity in modern development practices, where the interleaving of design and runtime activities is induced by the continuous evolution of software systems. In this context,…
Data analysis and monitoring of road networks in terms of reliability and performance are valuable but hard to achieve, especially when the analytical information has to be available to decision makers on time. The gathering and analysis of…
Automated, data-driven quality management systems, which facilitate the transformation of data into useable information, are desired to enhance decision-making processes. Integration of accurate, reliable, and straightforward approaches…
This study aims to evaluate four service-oriented architecture (SOA) system software development methodologies: business-driven development, model-driven development, event-driven development, and domain-driven development. These methods,…
Configuring and evolving dashboards in complex and large-scale Systems-of-Systems (SoS) can be an expensive and cumbersome task due to the many Key Performance Indicators (KPIs) that are usually collected and have to be arranged in a number…
As the Software as a Service (SaaS) paradigm continues to reshape the software industry, a nuanced understanding of its operational dynamics becomes increasingly crucial. This paper delves into the intricate relationship between pricing…
Software development comprises complex tasks which are performed by humans. It involves problem solving, domain understanding and communication skills as well as knowledge of a broad variety of technologies, architectures, and solution…
Component Based Approach has been introduced in core engineering discipline long back but the introduction to component based concept in software perspective is recently developed by Object Management Group. Its benefits from the…
Modern software development and operations rely on monitoring to understand how systems behave in production. The data provided by application logs and runtime environment are essential to detect and diagnose undesired behavior and improve…
DevOps is a modern software engineering paradigm that is gaining widespread adoption in industry. The goal of DevOps is to bring software changes into production with a high frequency and fast feedback cycles. This conflicts with software…
The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of…
AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime, entailing concerns about potential human rights…
Extracting valuable insights from vast amounts of information is a critical process that involves acquiring, storing, managing, analyzing, and visualizing data. Providing an abstract overview of data analytics applications is crucial to…
Technical debt (TD) is a metaphor to describe the trade-off between short-term workarounds and long-term goals in software development. Despite being widely used to explain technical issues in business terms, industry and academia still…
There is a growing need for better development methods and tools to keep up with the increasing complexity of new software systems. New types of user interfaces, the need for intelligent components, sustainability concerns, ... bring new…
The practical application of machine learning and data science (ML/DS) techniques present a range of procedural issues to be examined and resolve including those relating to the data issues, methodologies, assumptions, and applicable…
In the field of Prognostics and Health Management (PHM), recent years have witnessed a significant surge in the application of machine learning (ML). Despite this growth, the field grapples with a lack of unified guidelines and systematic…