28 citations · 29 across the 9 of their papers we have counts for
5 papers · 1 filter
Predicting Program Comprehension with Foundation Models of Human Cognition
Yannick Lehmen, Marvin Wyrich, Anna-Maria Maurer +2
Software engineering depends on the ability of developers to understand code, yet predicting how they do so remains an open challenge despite decades of research. Existing approach…
A Mechanistic Lens on Semantic Conflicts: Using Activation Patching to Understand LLM Behavior
Youssef Abdelsalam, Norman Peitek, Anna-Maria Maurer +2
Large language models (LLMs) are increasingly used in software-engineering tasks processing executable code and non-executable semantic cues such as comments or identifiers. These…
Neural Signatures of Programming Expertise: Classifying Programmer Skill Levels Using EEG Data
Maurice Rekrut, Mahima Mahabaleshwar Acharya, Taisiia Ulianova +5
Accurately assessing a programmer's skill level is critical for hiring, team composition, and performance evaluation in the software industry. Conventional methods, such as coding…
Harnessing Hype to Teach Empirical Thinking: An Experience With AI Coding Assistants
Marvin Wyrich, Norman Peitek, Kallistos Weis +1
Software engineering students often struggle to appreciate empirical methods and hypothesis-driven inquiry, especially when taught in theoretical terms. This experience report expl…
From Restructuring to Stabilization: A Large-Scale Experiment on Iterative Code Readability Refactoring with Large Language Models
Norman Peitek, Julia Hess, Sven Apel
Large language models (LLMs) are increasingly used for automated code refactoring tasks. Although these models can quickly refactor code, the quality may exhibit inconsistencies an…