Tootfinder

What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects
Mikel Robredo, Matteo Esposito, Fabio Palomba, Rafael Pe\~naloza, Valentina Lenarduzzi
https://arxiv.org/abs/2509.07763

What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects
Context. Code refactoring improves software quality without changing external behavior. Despite its advantages, its benefits are hindered by the considerable cost of time, resources, and continuous effort it demands. Aim. Understanding why developers refactor, and which metrics capture these motivations, may support wider and more effective use of refactoring in practice. Method. We performed a large-scale empirical study to analyze developers refactoring activity, leveraging Large Language Mod…

Unitree A2: Robuster Roboterhund für schwere Lasten läuft lange und schnell
Der Untitree A2 Stellar Hunter kann Lasten bis zu 100 kg tragen, ist äußerst robust und zugleich agil. Damit eignet er sich für ein breites Anwendungsspektrum.

Democrats seek information on Trump administration’s security clearance revocations
https://federalnewsnetwork.com/congress/2025/09/democrats-seek-information-on-trump-administrations-security-clearance-revocations/

A malicious Jira ticket can cause Cursor to exfiltrate secrets from the repository or local file system. But this is not just a problem with Cursor: GitHub MCP connections can also be exploited to expose private repository data, and a vulnerability in GitLab Duo allowed private information to be exposed through automatically rendered HTML code.

AgentFlayer: When a Jira Ticket Can Steal Your Secrets
TL;DR: A 0click attack through a malicious Jira ticket can cause Cursor to exfiltrate secrets from the repository or local file system.

EMORF-II: Adaptive EM-based Outlier-Robust Filtering with Correlated Measurement Noise
Arslan Majal, Aamir Hussain Chughtai, Muhammad Tahir
https://arxiv.org/abs/2509.07415 http…

EMORF-II: Adaptive EM-based Outlier-Robust Filtering with Correlated Measurement Noise
We present a learning-based outlier-robust filter for a general setup where the measurement noise can be correlated. Since it is an enhanced version of EM-based outlier robust filter (EMORF), we call it as EMORF-II. As it is equipped with an additional powerful feature to learn the outlier characteristics during inference along with outlier-detection, EMORF-II has improved outlier-mitigation capability. Numerical experiments confirm performance gains as compared to the state-of-the-art methods …

AR2: Attention-Guided Repair for the Robustness of CNNs Against Common Corruptions
Fuyuan Zhang, Qichen Wang, Jianjun Zhao
https://arxiv.org/abs/2507.06332

AR2: Attention-Guided Repair for the Robustness of CNNs Against Common Corruptions
Deep neural networks suffer from significant performance degradation when exposed to common corruptions such as noise, blur, weather, and digital distortions, limiting their reliability in real-world applications. In this paper, we propose AR2 (Attention-Guided Repair for Robustness), a simple yet effective method to enhance the corruption robustness of pretrained CNNs. AR2 operates by explicitly aligning the class activation maps (CAMs) between clean and corrupted images, encouraging the model…

check out the (new, not merged yet) #GlasgowInterfaceExplorer I²C controller documentation! https://whitequark.github.io/glasgow/refa…

Search-based Selection of Metamorphic Relations for Optimized Robustness Testing of Large Language Models
Sangwon Hyun, Shaukat Ali, M. Ali Babar
https://arxiv.org/abs/2507.05565 …

Search-based Selection of Metamorphic Relations for Optimized Robustness Testing of Large Language Models
Assessing the trustworthiness of Large Language Models (LLMs), such as robustness, has garnered significant attention. Recently, metamorphic testing that defines Metamorphic Relations (MRs) has been widely applied to evaluate the robustness of LLM executions. However, the MR-based robustness testing still requires a scalable number of MRs, thereby necessitating the optimization of selecting MRs. Most extant LLM testing studies are limited to automatically generating test cases (i.e., MRs) to en…

Improving Robustness of Foundation Models in Domain Adaptation with Soup-Adapters
Marco Roschkowski
https://arxiv.org/abs/2507.05807 https://

Improving Robustness of Foundation Models in Domain Adaptation with Soup-Adapters
In this paper, we tackle two fundamental problems in few-shot domain adaptation of foundation models. First, hyperparameter tuning is often impractical due to the lack of large validation datasets. Second, model robustness under distribution shifts where test time data deviates slightly from training distributions, remains a concern. We show that by training multiple independent adapters and averaging their outputs, the new model has a higher performance and is more robust to distribution shift…