From the 1 of 6 linked papers with an AI index.
6 papers
Predicting Program Comprehension with Foundation Models of Human Cognition
Yannick Lehmen, Marvin Wyrich, Anna-Maria Maurer +2
The paper evaluates a foundation model of human cognition, trained on psychological experiments, for predicting how developers understand code, showing it aligns better with human…
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…
Fixation-related potentials reveal that confusing program code elicits a late frontal positivity
Annabelle Bergum, Anna-Maria Maurer, Norman Peitek +5
As software pervades more and more areas of our professional and personal lives, there is an ever-increasing need to maintain software and for programmers to efficiently write and…
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…
How do Humans and LLMs Process Confusing Code?
Youssef Abdelsalam, Norman Peitek, Anna-Maria Maurer +2
Already today, humans and programming assistants based on large language models (LLMs) collaborate in everyday programming tasks. Clearly, a misalignment between how LLMs and progr…