most citedLearning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges

Ali Al-Kaswan, Maksim Plotnikov, Maxim Hájek +3

Large Language Model (LLM) agents are increasingly proposed for autonomous cybersecurity tasks, but their capabilities in realistic offensive settings remain poorly understood. We…

cs.SE20261 cited

Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment

Eileen Kapel, Jan Lennartz, Luis Cruz +2

Effective IT change management is important for businesses that depend on software and services, particularly in highly regulated sectors such as finance, where operational reliabi…

cs.LG2025

WaveStitch: Flexible and Fast Conditional Time Series Generation with Diffusion Models

Aditya Shankar, Lydia Y. Chen, Arie van Deursen +1

Generating temporal data under conditions is crucial for forecasting, imputation, and generative tasks. Such data often has metadata and partially observed signals that jointly inf…

cs.SE2025

A Qualitative Investigation into LLM-Generated Multilingual Code Comments and Automatic Evaluation Metrics

Jonathan Katzy, Yongcheng Huang, Gopal-Raj Panchu +5

Large Language Models are essential coding assistants, yet their training is predominantly English-centric. In this study, we evaluate the performance of code language models in no…

cs.SE2025

Code Red! On the Harmfulness of Applying Off-the-shelf Large Language Models to Programming Tasks

Ali Al-Kaswan, Sebastian Deatc, Begüm Koç +2

Nowadays, developers increasingly rely on solutions powered by Large Language Models (LLM) to assist them with their coding tasks. This makes it crucial to align these tools with h…