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Software model checking has experienced significant progress in the last two decades, however, one of its major bottlenecks for practical applications remains its scalability and adaptability. Here, we describe an approach to integrate…
Automotive engineering makes extensive use of numerical simulation throughout the design process. The development of numerical models, their validation against experimental tests, and their updating during vehicle and engine projects…
There have been many articles and mishaps published about the risks of uncontrolled spreadsheets in today's business environment, including non-compliance, operational risk, errors, and fraud all leading to significant loss events.…
As Machine Learning (ML) becomes more prevalent in Industry 4.0, there is a growing need to understand how systematic approaches to bringing ML into production can be practically implemented in industrial environments. Here, MLOps comes…
Context: DevOps and microservices are acknowledged to be important new paradigms to tackle contemporary software demands and provide capabilities for rapid and reliable software development. Industrial reports show that they are quickly…
Due to the fourth industrial revolution, industrial applications make use of the progress in communication and embedded devices. This allows industrial users to increase efficiency and manageability while reducing cost and effort.…
Big data analytics (BDA) applications use machine learning algorithms to extract valuable insights from large, fast, and heterogeneous data sources. New software engineering challenges for BDA applications include ensuring performance…
Context: Today's safety critical systems are increasingly reliant on software. Software becomes responsible for most of the critical functions of systems. Many different safety analysis techniques have been developed to identify hazards of…
As the role of information and communication technologies gradually increases in our lives, source code security becomes a significant issue to protect against malicious attempts Furthermore with the advent of data-driven techniques, there…
Information protection is becoming a focal point for designing, creating and implementing software applications within highly integrated technology environments. The use of a safe coding technique in the software development process is…
Analysis of the scientific literature showed that a lot of work is devoted to assessing the effectiveness of fire safety management in an enterprise. It is worth noting that today there is no universal method for the integrated assessment…
While manufacturers have been generating highly distributed data from various systems, devices and applications, a number of challenges in both data management and data analysis require new approaches to support the big data era. These…
Organizations that develop software have recognized that software process models are particularly useful for maintaining a high standard of quality. In the last decade, simulations of software processes were used in several settings and…
Models of software systems are used throughout the software development lifecycle. Dataflow diagrams (DFDs), in particular, are well-established resources for security analysis. Many techniques, such as threat modelling, are based on DFDs…
Traditionally, promoted by the internet companies, continuous delivery is more and more appealing to industries which develop systems with safety-critical functions. Since safety-critical systems must meet regulatory requirements and…
Context: DevOps can be defined as a cultural movement to improve and accelerate the delivery of business value by making the collaboration between development and operations effective. Although this movement is relatively recent, there…
Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This, in turn, allows analysts to compare alternative options to improve a business process. A common roadblock for…
Industrial Internet-of-Things (IIoT) results from the addition of sensing and actuating capabilities to industrial environments to improve the overall manufacturing processes. Some of these systems have highly-complex tasks of…
Artificial Intelligence for IT Operations (AIOps) describes the process of maintaining and operating large IT systems using diverse AI-enabled methods and tools for, e.g., anomaly detection and root cause analysis, to support the…
The popularity of open-source software (OSS) projects has grown significantly over the last few years with more organizations relying on them. As these projects become larger, the need for higher quality also increases. DevOps practices…