Tootfinder

TUM Teleoperation: Open Source Software for Remote Driving and Assistance of Automated Vehicles
Tobias Kerbl, David Brecht, Nils Gehrke, Nijinshan Karunainayagam, Niklas Krauss, Florian Pfab, Richard Taupitz, Ines Trautmannsheimer, Xiyan Su, Maria-Magdalena Wolf, Frank Diermeyer
https://arxiv.org/abs/2506.13933

TUM Teleoperation: Open Source Software for Remote Driving and Assistance of Automated Vehicles
Teleoperation is a key enabler for future mobility, supporting Automated Vehicles in rare and complex scenarios beyond the capabilities of their automation. Despite ongoing research, no open source software currently combines Remote Driving, e.g., via steering wheel and pedals, Remote Assistance through high-level interaction with automated driving software modules, and integration with a real-world vehicle for practical testing. To address this gap, we present a modular, open source teleoperat…

Ultra-low-resistivity nitrogen-doped p-type Cu2O thin films fabricated by reactive HiPIMS
Ji\v{r}\'i Rezek, Jan Koloros, Ji\v{r}\'i Hou\v{s}ka, Radom\'ir \v{C}erstv\'y, Stanislav Haviar, Jemal Yimer Damte, David Kolenat\'y, Pavel Baroch
https://arxiv.org/abs/2506.15275

Ultra-low-resistivity nitrogen-doped p-type Cu2O thin films fabricated by reactive HiPIMS
We have successfully fabricated the nitrogen-doped cuprous oxide thin films on the amorphous standard soda-lime glass by reactive high-power impulse magnetron sputtering. The energy of film-forming particles was controlled by the value of pulse-averaged target power density, which has a significant impact on the elemental composition, structure and optoelectrical properties of the films. We have shown that the high-energy regime is more suitable for preserving Cu2O structure and leads to contin…

AST-Enhanced or AST-Overloaded? The Surprising Impact of Hybrid Graph Representations on Code Clone Detection
Zixian Zhang, Takfarinas Saber
https://arxiv.org/abs/2506.14470

AST-Enhanced or AST-Overloaded? The Surprising Impact of Hybrid Graph Representations on Code Clone Detection
As one of the most detrimental code smells, code clones significantly increase software maintenance costs and heighten vulnerability risks, making their detection a critical challenge in software engineering. Abstract Syntax Trees (ASTs) dominate deep learning-based code clone detection due to their precise syntactic structure representation, but they inherently lack semantic depth. Recent studies address this by enriching AST-based representations with semantic graphs, such as Control Flow Gra…

Accurate Depth-Resolved Temperature Profiling via Thermal-Radiation Spectroscopy: Numerical Methods vs Machine Learning
Dmitrii Shymkiv, Zhongyuan Wang, Brigham Thornock, Aiden Karpf, Camila Nunez, Yuzhe Xiao
https://arxiv.org/abs/2506.14554

Accurate Depth-Resolved Temperature Profiling via Thermal-Radiation Spectroscopy: Numerical Methods vs Machine Learning
We present and compare three approaches for accurately retrieving depth-resolved temperature distributions within materials from their thermal-radiation spectra, based on: (1) a nonlinear equation solver implemented in commercial software, (2) a custom-built nonlinear equation solver, and (3) a deep neural network (DNN) model. These methods are first validated using synthetic datasets comprising randomly generated temperature profiles and corresponding noisy thermal-radiation spectra for three …

About explosive delayed desorption from methane-doped argon matrices
M. A. Bludov, I. V. Khyzhniy, S. A. Uyutnov, E. V. Savchenko
https://arxiv.org/abs/2506.14726

About explosive delayed desorption from methane-doped argon matrices
The features of delayed desorption from CH4-doped Ar matrices irradiated with an electron beam of subthreshold energy were studied. Radiolysis products were detected by emission spectroscopy. The total desorption yield was monitored by recording the pressure in the experimental chamber. Based on the analysis of the concentration dependence of delayed desorption bursts and their structure, an assumption was made about the formation of CH4 clusters in Ar matrices. At a high dopant concentration o…

Anticipating Bugs: Ticket-Level Bug Prediction and Temporal Proximity Effects
Daniele La Prova, Emanuele Gentili, Davide Falessi
https://arxiv.org/abs/2506.14290

Anticipating Bugs: Ticket-Level Bug Prediction and Temporal Proximity Effects
The primary goal of bug prediction is to optimize testing efforts by focusing on software fragments, i.e., classes, methods, commits (JIT), or lines of code, most likely to be buggy. However, these predicted fragments already contain bugs. Thus, the current bug prediction approaches support fixing rather than prevention. The aim of this paper is to introduce and evaluate Ticket-Level Prediction (TLP), an approach to identify tickets that will introduce bugs once implemented. We analyze TLP at t…

As long as it's sufficiently "debugged" 😉
Food as Software (FaS)
#futurism

Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection
David Farr, Kevin Talty, Alexandra Farr, John Stockdale, Iain Cruickshank, Jevin West
https://arxiv.org/abs/2506.10104

Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection
As cyber threats become more sophisticated, rapid and accurate vulnerability detection is essential for maintaining secure systems. This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weakness Enumerations (CWEs), comparing zero-shot, few-shot cross-domain, and few-shot in-domain prompting strategies. Our results indicate that while zero-shot prompting performs poorly, few-shot prompti…

"You are a software worker. And you are able to do your job in more or less good conditions only because you are standing on the shoulders of countless others. We are talking about the fact that if you have 20 days of holidays, if you only work 5 days a week, if you are able to take some days or weeks off after the birth of your child, it is because some others before you have sacrificed their lives so you can work in decent conditions."

ASTAGEN: Empirical Evaluation of Automated SATD Taxonomy Generation with LLMs
Sota Nakashima, Yuta Ishimoto, Masanari Kondo, Tao Xiao, Yasutaka Kamei
https://arxiv.org/abs/2506.09601

ASTAGEN: Empirical Evaluation of Automated SATD Taxonomy Generation with LLMs
Technical debt refers to suboptimal code that degrades software quality. When developers intentionally introduce such debt, it is called self-admitted technical debt (SATD). Since SATD hinders maintenance, identifying its categories is key to uncovering quality issues. Traditionally, constructing such taxonomies requires manually inspecting SATD comments and surrounding code, which is time-consuming, labor-intensive, and often inconsistent due to annotator subjectivity. This study presents ASTA…