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Abstract: Federated Learning (FL) has emerged as a promising approach for collaborative machine learning, addressing data privacy concerns. As data security and privacy concerns continue to gain prominence, FL stands out as an option to enable organizations to leverage collective knowledge without compromising sensitive data. However, existing FL platforms and frameworks often present challenges for softwar...
Abstract: In recent years, Federated Learning, as an approach to distributed learning, has shown its potential with the increasing number of devices on the edge and the development of computing power. The method enables large-scale training on the device that creates the data but with the sensitive data remaining within the data’s owner. In reality, however, the vast majority of enterprises have the problem...
Abstract: In recent years, the interest in applying machine learning (ML) and deep learning (DL) has been increasing due to their ability to learn to predict and find structure in data. The most common approach of ML and DL is supervised learning. Supervised learning requires the input data to be labeled. However, as reported by many industries, such as the embedded systems domain, fully labeled datasets ar...
Abstract: Testing of a large-scale and complex software system requires many types of knowledge, skills and personality traits. Contrasting the idea of a perfect all-round tester, this paper presents the Testing Hopscotch model with six complementary profiles, and the key characteristics considered as most relevant for each profile. The model is based on 60 interviews with engineers from three large-scale c...
Abstract: Safety systems, i.e., systems whose malfunction can result in catastrophic consequences, are usually designed with redundancy in mind to reach high levels of reliability. However, Common Cause Failures (CCF), i.e., single failure events affecting multiple components or functions in a system, can threaten the desired reliability. To solve this problem, practitioners must use proven methods, such as...
Abstract: Background: In-situ marine data has a low reusability rate, primarily due to differences in data usage objectives among stakeholders in data ecosystems. The extreme cost of collecting and maintaining in-situ marine data threatens the sustainable usage of the ocean. Aims: This paper provides an overview of current data and data quality (DQ) requirements. We also investigate limitations in the curre...
Abstract: Welcome to SST'23, the 11th International Workshop on Software and Systems Traceability as part of the program of the 31st IEEE International Conference on Requirements Engineering (RE 2023) in Hanover, Germany! SST'23 is held on Monday, September 4, 2023. We are very happy to host an exciting event with an engaging, varied, and high-quality program and to continue the tradition of previous editio...
Abstract: VeriDevOps offers a methodology and a set of integrated mechanisms that significantly improve automation in DevOps to protect systems at operations time and prevent security issues at development time by (1) specifying security requirements, (2) generating trace monitors, (3) locating root causes of vulnerabilities, and (4) identifying security flaws in code and designs. This paper presents a meth...
Abstract: Understanding the behaviour of a system’s API can be hard. Giving users access to relevant examples of how an API behaves has been shown to make this easier for them. In addition, such examples can be used to verify expected behaviour or identify unwanted behaviours. Methods for automatically generating examples have existed for a long time. However, state-of-the-art methods rely on either white-b...
Abstract: Multi-core architectures have grown to be a popular choice for deploying Mixed Criticality Systems (MCS). The focus of research in MCS has been to provide timing assurances for jobs with different criticality levels. Due to their significant processing demands and energy-aware/constrained nature, energy conservation in these systems is becoming mandatory. This article presents, mcDVFS, an energy m...
Abstract: There is a growing body of knowledge on network intrusion detection, and several open data sets with network traffic and cyber-security threats have been released in the past decades. However, many data sets have aged, were not collected in a contemporary industrial communication system, or do not easily support research focusing on distributed anomaly detection. This paper presents the Westermo n...
Abstract: Autoencoder is a widely used deep learning method, which first extracts features from all data through unsupervised reconstruction, and then fine-tunes the network with labeled data. However, due to the limited number of labeled data samples, the network may lack sufficient generalization ability and is prone to overfitting. This article proposes a new semisupervised deep learning method called fe...
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