Annabelle Bergum

Annabelle Bergum, Anna-Maria Maurer, Norman Peitek et al.

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 understand program code. In the first study of its kind, we analyze fixation-related potentials (FRPs) to explore the online processing of program code patterns that are confusing to programmers, but not to the computer (so-called atoms of confusion), and their underlying neurocognitive mechanisms in an ecologically valid setting. Relative to clean counterparts in program code without an atom of confusion, confusing code elicits a late frontal positivity of about 400 to 700 ms after first looking at the atom of confusion. This frontal positivity resembles an event-related potential (ERP) component found during natural language processing that is elicited by unexpected but plausible words in sentence context. Thus, we suggest that the brain engages similar neurocognitive mechanisms in response to unexpected and informative inputs in program code and in natural language. In both domains, these inputs update a comprehender's situation model, which is essential for information extraction from a quickly unfolding input. Our results have far-reaching implications for programming and pave the way for interdisciplinary collaborations between software engineering and psycholinguistics.

Maurice Rekrut, Mahima Mahabaleshwar Acharya, Taisiia Ulianova et al.

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 tests or interviews, often fail to capture the full spectrum of cognitive abilities underlying programming expertise. This study explores using electroencephalography (EEG) and machine learning to investigate neural correlates of programming skill. We analyzed an existing EEG dataset recorded during code comprehension from 37 programmers with 1 to 30 years of experience (8.1 +/- 6.3 years) to examine relationships between neural activity and expertise. Additionally, we conducted classification experiments using Random Forest classifiers with diverse features for binary (experts vs. novices) and multi-class (experts, intermediates, novices) setups.We identified EEG features and brain regions associated with programming expertise. Specifically, EEG entropy showed the strongest correlation with skill level. Furthermore, experts' brains were characterized by highly localized centro-frontal activation, whereas frontal activation in other groups was part of a more distributed network. Regarding classification, our setup achieved an average accuracy of 91.83% (binary) and 78.15% (multi-class) in stratified 10-fold cross-validation, while leave-one-subject-out validation achieved 85.00% and 58.80%, respectively. Individual frequency bands outperformed full-spectrum analyses, and both program comprehension and resting-state data yielded strong results. These findings demonstrate that EEG features effectively capture neural correlates across different skill levels and highlight the potential of neural data to complement traditional methods of skill assessment.