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Abstract: Using interviews, we investigated the practices and toolchains for machine learning (ML)-enabled systems from 16 organizations across various domains in Finland. We observed some well-established artificial intelligence engineering approaches, but practices and tools are still needed for the testing and monitoring of ML-enabled systems....
Abstract: Supporting customers after they acquire the prod-uct is essential for companies producing and selling software-intensive embedded systems products. Generally, customer sup-port is the first interaction point between the product users and the product vendor. Customer support is often engaged with answering customers' questions, troubleshooting, fault identification, and fixing product faults. While...
Abstract: Take Home Message We did not see evidence of an effect on the detection rate or the risk difference for prostate cancer between manually operated and robot-assisted in-bore magnetic resonance imaging targeted biopsy. Cost effectiveness should be considered carefully when choosing the biopsy system....
Abstract: Being used in key features, such as sensing and intelligent path planning, Artificial Intelligence (AI) has become an inevitable part of automated vehicles (AVs). However, their usage in the automotive industry always comes with a “label” that questions their impact on the overall AV safety. This paper focuses on the safe deployment of AI-based AVs. Among the various ways for ensuring the safety o...
Abstract: Artificial Intelligence (AI), and especially machine learning can be used to find statistical patterns in datasets with thousands of variables with ease. But an understanding of causality is difficult to learn for a machine. For humans however, realising causal relations is often not a difficult process, as we can refer to experience or scientific knowledge. Here we propose the use of structural c...
Abstract: This paper presents Nodeguard, a security approach for detecting and isolating misbehaving Virtual Machines (VMs) in multi-tenant virtualized cloud data centers, based on the Virtual Machine Introspection (VMI) monitoring primitives. Nodeguard employs a divide-and-conquer strategy that checks logical groups of VMs to ensure the efficiency of the detection mechanisms which opportunistically approac...
Abstract: Self-adaptive systems typically operate in heterogeneous environments and need to optimize their behavior based on a variety of quality attributes to meet stakeholders’ needs. During adaptation planning, these quality attributes are considered in the form of constraints, describing requirements that must be fulfilled, and utility functions, which are used to select an optimal plan among several al...
Abstract: Teaching Scrum using Lego has been an established teaching technique for years. However, the COVID-19 pandemic forced teachers all over the globe to rethink this valuable teaching tool. In this experience report, we show how we transferred our version of a Lego Scrum workshop into the world of Minetest, an open-source variant of Minecraft. We detail our reasoning, the concrete technical and pedago...
Abstract: Adequate testing of safety-critical systems is vital to ensure correct functional and non-functional operations. Previous research has shown that testing such systems requires a lot of effort, thus automated testing techniques have found a certain degree of success. However, automated testing has not replaced the need for manual testing, rather a common industrial practice exhibits a balance betwe...
Abstract: This paper gives an overview of the software engineering activities of Siemens Healthineers that are related to education and learning. Our training activities have a long history and are done globally throughout the company. We expect that experience and lessons learned are useful for others. Our software engineering education activities range from onboarding of new employees to approaches for co...
Abstract: Deep learning (DL) based software systems are difficult to develop and maintain in industrial settings due to several challenges. Data management is one of the most prominent challenges which complicates DL in industrial deployments. DL models are data-hungry and require high-quality data. Therefore, the volume, variety, velocity, and quality of data cannot be compromised. This study aims to explo...
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