Near infrared spectroscopy (NIRS) has been used as a low cost, noninvasive method to measure brain activity. In this paper, we experiment to measure the effects of variables and controls in a source code to the brain activity in program comprehension. The measurement results are evaluated after noise reduction and normalization to statistical analysis. As the result of the experiment, significant differences in brain activity were observed at a task that requires memorizing variables to understand a code snippet. On the other hand, no significant differences between different levels of mental arithmetic tasks were observed. We conclude that the frontal pole reflects workload to short-term memory caused by variables without affected from calculation.

Expertise enables humans to achieve outstanding performance on domain-specific tasks, and programming is no exception. Many have shown that expert programmers exhibit remarkable differences from novices in behavioral performance, knowledge structure, and selective attention. However, the underlying differences in the brain are still unclear. We here address this issue by associating the cortical representation of source code with individual programming expertise using a data-driven decoding approach. This approach enabled us to identify seven brain regions, widely distributed in the frontal, parietal, and temporal cortices, that have a tight relationship with programming expertise. In these brain regions, functional categories of source code could be decoded from brain activity and the decoding accuracies were significantly correlated with individual behavioral performances on source-code categorization. Our results suggest that programming expertise is built up on fine-tuned cortical representations specialized for the domain of programming.

Expertise enables humans to achieve outstanding performance on domain-specific tasks, and programming is no exception. Many studies have shown that expert programmers exhibit remarkable differences from novices in behavioral performance, knowledge structure, and selective attention. However, the underlying differences in the brain of programmers are still unclear. We here address this issue by associating the cortical representation of source code with individual programming expertise using a data-driven decoding approach. This approach enabled us to identify seven brain regions, widely distributed in the frontal, parietal, and temporal cortices, that have a tight relationship with programming expertise. In these brain regions, functional categories of source code could be decoded from brain activity and the decoding accuracies were significantly correlated with individual behavioral performances on a source-code categorization task. Our results suggest that programming expertise is built upon fine-tuned cortical representations specialized for the domain of programming. Significance StatementThe expertise needed for programming has attracted increasing interest among researchers and educators in our computerized world. Many studies have demonstrated that expert programmers exhibit superior behavioral performance, knowledge structure, and selective attention; but how their brain accommodates such superiority is not well understood. In this paper we have recorded brain activities from subjects covering a wide range of programming expertise. The results show that functional categories of source code can be 1 decoded from their brain activity and the decoding accuracies on the seven brain regions in frontal, parietal, temporal cortices are significantly correlated with individual behavioral performances. This study provides evidence that outstanding performances of expert programmers are associated with domain-specific cortical representations in these widely distributed brain areas.

Near infrared spectroscopy (NIRS) has been used as a low cost, noninvasive method to measure brain activity. In this paper, we measure the effects of variables and controls in a source code on brain activity during program comprehension. The measurement results are evaluated after noise reduction and normalization to statistical analysis. As the result of the experiment, significant differences in brain activity were observed at a task that requires memorizing variables to understand a code snippet. On the other hand, no significant difference was observed between different levels of mental arithmetic tasks. We conclude that the frontal pole reflects workload to short-term memory caused by variables without affected from calculation.

Program comprehension is a dominant process in software development and maintenance. Experts are considered to comprehend the source code efficiently by directing their gaze, or attention, to important components in it. However, reflecting the importance of components is still a remaining issue in gaze behavior analysis for source code comprehension. Here we show a conceptual framework to compare the quantified importance of source code components with the gaze behavior of programmers. We use "attention" in attention models (e.g., code2vec) as the importance indices for source code components and evaluate programmers' gaze locations based on the quantified importance. In this report, we introduce the idea of our gaze behavior analysis using the attention map, and the results of a preliminary experiment.