Format

Context

Unmanned aerial vehicles (UAVs), also known as drones, are acquiring increasing autonomy. With their commercial adoption, the problem of testing their safety requirements has become a critical concern. Simulation-based testing represents a fundamental practice for cost-effective testing of UAVs.

Context

Android applications often process sensitive data such as location, contacts, and authentication tokens. Ensuring that this information is not leaked or misused is a central challenge in mobile app security.

Taint analysis is a static or dynamic program analysis technique that tracks the flow of sensitive data (“tainted sources”) through a program to determine whether it reaches untrusted components (“sinks”). Several tools exist to perform taint analysis on Android applications, including FlowDroid, Mariana Trench, and Joern. Each has different capabilities, rule definitions, and performance characteristics.

Context

Merge conflicts are a common challenge in collaborative software development, requiring developers to manually resolve inconsistencies between different code versions. Prior research has explored automated approaches to merge conflict resolution, but the impact of developer behavior and personas on the merge process remains not fully investigated [1].

Context

Autonomous vehicles (AVs) are complex cyberphysical systems that require extensive testing to ensure safety. Since field testing is costly and unsafe, simulation-based testing using platforms like CARLA and BeamNG.tech has become a cornerstone in AV software validation. These simulators rely heavily on GPU performance for rendering, physics, and sensor emulation, and are therefore both resource-intensive and energy-demanding. As the scale of simulation campaigns grows (thousands of tests per day in CI pipelines), understanding and optimizing GPU cost becomes critical for cost-effective and sustainable testing.

Context

The increasing adoption of continuous integration and continuous deployment (CI/CD) practices has transformed software development, with GitHub Actions playing a key role in automating workflows. Many projects rely on third-party GitHub Actions, which streamline deployment but also introduce security vulnerabilities due to outdated dependencies, excessive permissions, or lack of maintenance.

Despite the availability of security mechanisms such as Dependabot alerts and the GitHub Advisory Database, vulnerabilities often remain unpatched for long periods, leaving repositories exposed to supply chain attacks. Understanding how developers address, prioritize, or neglect these vulnerabilities is key to improving security practices in CI/CD environments.

Context

Unmanned aerial vehicles (UAVs), also known as drones, are acquiring increasing autonomy. With their commercial adoption, the problem of testing their safety requirements has become a critical concern. Simulation-based testing represents a fundamental practice, but the testing scenarios considered in software-in-the-loop testing may be different from the actual scenarios experienced in the field.

Context

Large Language Models (LLMs) have shown strong performance in code generation, completion, and repair tasks. However, supervised pretraining on massive code corpora is limited by data quality, lack of explicit feedback, and the inability to capture correctness beyond next-token prediction. Recent research has explored Reinforcement Learning (RL) based training approaches to refine LLMs for code. By leveraging feedback signals—such as compilation success, test case execution, or static analysis warnings—models can be trained to better align with correctness and developer intent.

Context

Unmanned aerial vehicles (UAVs), also known as drones, are acquiring increasing autonomy. With their commercial adoption, the problem of testing their safety requirements has become a critical concern. Simulation-based testing represents a fundamental practice for cost-effective testing of UAVs.

Context

Android applications often process sensitive data such as location, contacts, and authentication tokens. Ensuring that this information is not leaked or misused is a central challenge in mobile app security.

Taint analysis is a static or dynamic program analysis technique that tracks the flow of sensitive data (“tainted sources”) through a program to determine whether it reaches untrusted components (“sinks”). Several tools exist to perform taint analysis on Android applications, including FlowDroid, Mariana Trench, and Joern. Each has different capabilities, rule definitions, and performance characteristics.

Context

Merge conflicts are a common challenge in collaborative software development, requiring developers to manually resolve inconsistencies between different code versions. Prior research has explored automated approaches to merge conflict resolution, but the impact of developer behavior and personas on the merge process remains not fully investigated [1].

Context

Autonomous vehicles (AVs) are complex cyberphysical systems that require extensive testing to ensure safety. Since field testing is costly and unsafe, simulation-based testing using platforms like CARLA and BeamNG.tech has become a cornerstone in AV software validation. These simulators rely heavily on GPU performance for rendering, physics, and sensor emulation, and are therefore both resource-intensive and energy-demanding. As the scale of simulation campaigns grows (thousands of tests per day in CI pipelines), understanding and optimizing GPU cost becomes critical for cost-effective and sustainable testing.

Context

The increasing adoption of continuous integration and continuous deployment (CI/CD) practices has transformed software development, with GitHub Actions playing a key role in automating workflows. Many projects rely on third-party GitHub Actions, which streamline deployment but also introduce security vulnerabilities due to outdated dependencies, excessive permissions, or lack of maintenance.

Despite the availability of security mechanisms such as Dependabot alerts and the GitHub Advisory Database, vulnerabilities often remain unpatched for long periods, leaving repositories exposed to supply chain attacks. Understanding how developers address, prioritize, or neglect these vulnerabilities is key to improving security practices in CI/CD environments.

Context

Unmanned aerial vehicles (UAVs), also known as drones, are acquiring increasing autonomy. With their commercial adoption, the problem of testing their safety requirements has become a critical concern. Simulation-based testing represents a fundamental practice, but the testing scenarios considered in software-in-the-loop testing may be different from the actual scenarios experienced in the field.

Context

Large Language Models (LLMs) have shown strong performance in code generation, completion, and repair tasks. However, supervised pretraining on massive code corpora is limited by data quality, lack of explicit feedback, and the inability to capture correctness beyond next-token prediction. Recent research has explored Reinforcement Learning (RL) based training approaches to refine LLMs for code. By leveraging feedback signals—such as compilation success, test case execution, or static analysis warnings—models can be trained to better align with correctness and developer intent.

Context

Large language models (LLMs) have became popular over the last few years, one of the reason being the quality of the outputs these models generate. LLM Agents are a step further, they allow LLMs to use memory, tools and sequential thinking.

Context

Managing distressed patients in clinical environments is a challenging yet critical skill for healthcare professionals. Patients exhibit diverse emotional responses—ranging from anxiety and shyness to outright resistance-making it essential for clinicians to adapt their approach. Traditional training methods often lack the realism needed to prepare professionals for these high-stakes interactions.

Context

Unmanned aerial vehicles (UAVs), also known as drones, are acquiring increasing autonomy. With their commercial adoption, the problem of testing their safety requirements has become a critical concern. Simulation-based testing represents a fundamental practice for cost-effective testing of UAVs.

Context

Android applications often process sensitive data such as location, contacts, and authentication tokens. Ensuring that this information is not leaked or misused is a central challenge in mobile app security.

Taint analysis is a static or dynamic program analysis technique that tracks the flow of sensitive data (“tainted sources”) through a program to determine whether it reaches untrusted components (“sinks”). Several tools exist to perform taint analysis on Android applications, including FlowDroid, Mariana Trench, and Joern. Each has different capabilities, rule definitions, and performance characteristics.

Context

The Nostr protocol has rapidly emerged as a foundation for decentralized, censorship-resistant communication. Its lightweight, open design enables anyone to run a relay or client, fostering a global, permissionless network. Extending Nostr’s principles to search infrastructure unlocks the possibility of a truly distributed search engine—where billions of mobile devices collaboratively crawl, index, and serve web content without reliance on centralized servers. This approach promises not only greater privacy and resilience, but also customizable ranking and open participation, fundamentally reimagining how information is discovered and accessed online.

Context

Large language models (LLMs) have became popular over the last few years, one of the reason being the quality of the outputs these models generate. Recent advancements try to make models think more, by either utilizing simple prompts or by training them using self-reflection via reinforcement learning.

Context

Autonomous vehicles (AVs) are complex cyberphysical systems that require extensive testing to ensure safety. Since field testing is costly and unsafe, simulation-based testing using platforms like CARLA and BeamNG.tech has become a cornerstone in AV software validation. These simulators rely heavily on GPU performance for rendering, physics, and sensor emulation, and are therefore both resource-intensive and energy-demanding. As the scale of simulation campaigns grows (thousands of tests per day in CI pipelines), understanding and optimizing GPU cost becomes critical for cost-effective and sustainable testing.

Context

We work at the intersection of human-robot interaction, Mixed Reality interfaces, and collaborative cyber-physical systems. Modern robotics needs seamless collaboration between humans and robot swarms in shared workspaces. This project operates within the SwarmOps research framework, focusing on human-sensing based MLOps for collaborative cyber-physical systems. Current developments in VR/MR technology provide new opportunities for rich human tracking that can inform adaptive robot behavior.

Context

In present-day times of climate change, all industry sectors should bring down their green house gas emissions (carbon footprint). However, the IT sector’s emissions increase unchecked. The research area for this seminar thesis is the field of “green” software engineering. In this research area one aims at finding ways to lower the energy consumption of a piece of source code when run as a program. Using specialized registers (so called model specific registers) that deliver the cumulated energy consumption of the CPU one can track back energy consumption to different parts of a program.

Context

Large Language Models (LLMs) have shown strong performance in code generation, completion, and repair tasks. However, supervised pretraining on massive code corpora is limited by data quality, lack of explicit feedback, and the inability to capture correctness beyond next-token prediction. Recent research has explored Reinforcement Learning (RL) based training approaches to refine LLMs for code. By leveraging feedback signals—such as compilation success, test case execution, or static analysis warnings—models can be trained to better align with correctness and developer intent.

Context

This project addresses the fundamental challenge of programming and controlling robot swarms through intuitive interfaces. We operate in the domain of Mixed Reality robotics interfaces, spatial computing, and multi-robot coordination systems. Traditional robot programming requires complex coding or abstract 2D interfaces that disconnect users from the spatial nature of robotic coordination. This work contributes to the broader SwarmOps research initiative by developing the human-machine interface components necessary for effective human oversight of collaborative cyber-physical systems.